Information processing system, information processing method, and information processing program

Abstract

According to one embodiment of the present invention, an information processing system is provided. The information processing system includes at least one processor capable of executing a program to perform the following steps. In the acquisition step, a first estimated value relating to the physical properties of a substance calculated by a predetermined physical property simulation based on a model of the substance having a strongly ordered phase and a measured value obtained by measuring the substance are acquired. Here, the first estimation value contains a temperature dependency of an order variable in the strongly ordered phase and a coupling coefficient representing a magnitude of an interaction between sites of a substance contributing to the formation of the strongly ordered phase. In the data assimilation processing step, if the measurement value includes the measurement reference value, the ratio of the measurement reference value to the estimation reference value is multiplied by the coupling coefficient included in the acquired first estimation value in accordance with the number of times indicating the dependency of the estimation reference value with respect to the coupling coefficient, and the coupling coefficient included in the acquired second estimation value is multiplied by the number of times indicating the dependency of the estimation reference value with respect to the coupling coefficient. Thus, first data assimilation processing for the coupling coefficient is performed. Here, the measurement reference value includes at least one of a phase transition temperature indicating a phase transition from the strongly ordered phase due to the value of the sequence parameter being 0, and a saturation value corresponding to the value of the sequence parameter corresponding to the saturation state of the strongly ordered phase at absolute zero degree. The estimation reference value is a value corresponding to the measurement reference value among the phase transition temperature and the saturation value included in the acquired first estimation value. In the output step, the coupling coefficient subjected to the first data assimilation processing is output as a second estimated value.

Description

Technical Field

[0001] The present invention relates to an information processing system, an information processing method and an information processing program. Background Art

[0002] As a prior art, Patent Document 1 discloses a saturation magnetization prediction method and a saturation magnetization prediction simulation program, which can easily calculate the saturation magnetization of a single-phase magnetic phase at a finite temperature.

[0003] The magnetization prediction method includes: a first step of substituting measured data of saturation magnetization at a finite temperature into Kuzmin's formula to calculate the saturation magnetization and Curie temperature at absolute zero; a second step of assimilating the saturation magnetization and Curie temperature at absolute zero calculated in the first step with the saturation magnetization and Curie temperature at absolute zero calculated by first-principles calculation, and calculating a prediction model formula represented by a function of the existence ratio of elements constituting a single-phase magnetic phase for each of the saturation magnetization and Curie temperature at absolute zero using machine learning; and a third step of applying the prediction model formula generated in the second step to Kuzmin's formula to calculate the saturation magnetization at a finite temperature.

[0004] Prior art literature

[0005] Patent Literature

[0006] Patent Document 1: Japanese Patent Application Publication No. 2021-33964 Summary of the invention

[0007] Technical problem to be solved by the invention

[0008] However, for substances with a strongly ordered phase, the estimated value based on the physical property simulation is sometimes different from the actual measured value. The main reason for the difference between the estimated value and the measured value is a variety of factors such as the purity and shape of the substance caused by the measurement sample, the factors caused by the approximation when performing the physical property simulation, and sometimes it varies depending on the state of the measurement sample, the measurement conditions, etc. Therefore, there is still room for improvement in the technology of introducing the main reason for the difference between the estimated value and the measured value into the estimated value.

[0009] Technical solutions for solving technical problems

[0010] According to one embodiment of the present invention, an information processing system is provided. In the information processing system, at least one processor capable of executing a program to perform the following steps is provided. In the acquisition step, a first estimated value related to the physical property of the substance calculated by a predetermined physical property simulation based on a model of a substance having a strongly ordered phase and a measured value obtained by measuring the substance are acquired. Here, the first estimated value includes the temperature dependence of the order parameter in the strongly ordered phase and the coupling coefficient representing the magnitude of the interaction between the sites of the substance that contributes to the formation of the strongly ordered phase. In the data assimilation processing step, when the measured value includes a measured reference value, the ratio of the measured reference value to the estimated reference value is multiplied by the coupling coefficient included in the acquired first estimated value according to the number of times representing the dependence of the estimated reference value on the coupling coefficient, thereby performing the first data assimilation processing on the coupling coefficient. Among them, the measured reference value includes at least one of a phase transition temperature and a saturation value, the phase transition temperature represents a phase transition from a strongly ordered phase caused by the value of the order parameter being 0, and the saturation value is the value of the order parameter corresponding to the saturation state of the strongly ordered phase at absolute zero. The estimated reference value is a value corresponding to the measurement reference value among the phase change temperature and the saturation value included in the acquired first estimated value. In the output step, the coupling coefficient subjected to the first data assimilation process is output as the second estimated value.

[0011] Effects of the Invention

[0012] According to such a structure, the first estimated value includes information related to the ideal physical properties of the substance to be measured. The measured value includes information inherent to the individual substance of the substance to be measured, such as the quality of the substance and the measurement conditions. Therefore, the second estimated value calculated based on the first estimated value and the measured value becomes a value that reflects the information inherent to the individual substance relative to the ideal physical properties of the substance.

[0013] Here, the coupling coefficient indicates the strength of the interaction between sites that form a strongly ordered phase, and is therefore an important factor in determining the properties of the strongly ordered phase, such as the physical property values ​​resulting from the strongly ordered phase and the spatial properties formed by magnetic domains.

[0014] Therefore, by improving the accuracy of the coupling coefficient through the above-mentioned data assimilation, it is possible to suppress the divergence between the results of the physical property simulation related to the strongly ordered phase and the measurement results of the substance. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a diagram showing the structure of the information processing system 1 .

[0016] Figure 2 It is a block diagram showing a hardware block diagram of the information processing system 2 .

[0017] Figure 3A block diagram showing the hardware configuration of the user terminal 3.

[0018] Figure 4 This is a diagram showing an example of the functional units included in the processor 23 .

[0019] Figure 5 This is a flowchart showing an example of the flow of information processing executed in the information processing system 1 .

[0020] Figure 6 It is a flowchart showing the flow of data assimilation processing.

[0021] Figure 7 It is a flowchart showing the details of the process of step S100.

[0022] Figure 8 3 is a diagram showing the change in the temperature dependency of the spontaneous magnetization M caused by the data assimilation in step 103 .

[0023] Fig. 9 : is a graph showing the change in the temperature dependency of the spontaneous magnetization M caused by the data assimilation in step S110 .

[0024] Fig.10 It is a flowchart showing the details of the process of step S200.

[0025] Fig.11 : is a graph showing the change in the temperature dependency of the magnetic anisotropy energy K caused by the data assimilation in step S202.

[0026] Fig.12 : is a graph showing the change in the temperature dependency of the second estimated magnetic anisotropy energy K2 due to the correction in step S204.

[0027] Fig.13 It is a flowchart showing the details of the process of step S300.

[0028] Fig.14 It is a flowchart showing the details of the process in step S305. DETAILED DESCRIPTION

[0029] Hereinafter, embodiments of the present invention will be described using the drawings. Various characteristic features described in the embodiments described below can be combined with each other.

[0030] In addition, the program for implementing the software appearing in this embodiment can be provided as a computer-readable non-transitory recording medium (Non-Transitory Computer-Readable Medium), can be provided in a form that can be downloaded from an external server, and can be provided in a form that the program is started by an external computer and its function is implemented through a client terminal (so-called cloud computing).

[0031] In addition, in this embodiment, the "unit" may include, for example, a combination of hardware resources implemented by a generalized circuit and information processing of software that can be specifically implemented by these hardware resources. In addition, in this embodiment, various information is processed, and this information is represented, for example, by a physical value representing a signal value of voltage / current, a high or low signal value as a bit collection of a binary number composed of 0 or 1, or a quantum superposition (so-called quantum bit), and this information can perform communication / operation on a generalized circuit.

[0032] In addition, a circuit in a broad sense refers to a circuit realized by at least appropriately combining a circuit, a circuit system, a processor, and a memory, etc. That is, it includes an application-specific integrated circuit (ASIC), a programmable logic device (for example, a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)).

[0033] 1. Hardware structure

[0034] In this section, the hardware structure is described.

[0035] <Information processing system 1>

[0036] Figure 11 is a block diagram showing an information processing system 1. The information processing system 1 includes an information processing device 2 and a user terminal 3. The information processing device 2 and the user terminal 3 are configured to be able to communicate via an electrical communication line. In one embodiment, the information processing system 1 is composed of one or more devices or components. For example, if the information processing system 1 is composed of only the information processing device 2, the information processing system 1 may be the information processing device 2. These components are described below.

[0037] <Information Processing Device 2>

[0038] Figure 2 2 is a block diagram showing the hardware configuration of the information processing device 2. The information processing device 2 includes a communication unit 21, a storage unit 22, and a processor 23, and these components are electrically connected via a communication bus 20 inside the information processing device 2. Each component will be described further.

[0039] The communication unit 21 is preferably a wired communication unit such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., but may also include wireless LAN network communication, mobile communication such as 3G / LTE / 5G, BLUETOOTH (registered trademark) communication, etc. as needed. That is, it is more preferably implemented as a collection of these multiple communication units. That is, the information processing device 2 can also communicate various information from the outside via the communication unit 21 and the network.

[0040] The storage unit 22 stores various information defined by the above description. This can be implemented as, for example, a storage device such as a solid state drive (SSD) that stores various programs, etc. related to the information processing device 2 executed by the processor 23, or a memory such as a random access memory (RAM) that stores temporarily required information (self-parameters, arrays, etc.) related to the calculation of the program. The storage unit 22 stores various programs, parameters, etc. related to the information processing device 2 executed by the processor 23.

[0041] The processor 23 processes / controls the overall actions associated with the information processing device 2. The processor 23 is, for example, a central processing unit (CPU) not shown in the figure. The processor 23 implements various functions of the information processing device 2 by reading out a specified program stored in the storage unit 22. That is, the information processing involved in the software stored in the storage unit 22 is specifically implemented by the processor 23 as an example of hardware, thereby enabling the execution of each functional unit included in the processor 23. These will be further described in detail in the next section. In addition, the processor 23 is not limited to a single processor, and can also be implemented as having multiple processors 23 according to function. In addition, it can also be a combination of them.

[0042] <User Terminal 3>

[0043] Figure 3 3 is a block diagram showing the hardware configuration of the user terminal 3. The user terminal 3 includes a communication unit 31, a storage unit 32, a processor 33, a display unit 34, and an input unit 35, and these components are electrically connected inside the user terminal 3 via a communication bus 30. The description of the communication unit 31, the storage unit 32, and the processor 33 is the same as the description of each unit in the information processing device 2, and is therefore omitted.

[0044] The display unit 34 may be included in the housing of the user terminal 3 or may be external. The display unit 34 displays a graphical user interface (GUI) screen that can be operated by the user. This is preferably implemented by using display devices such as a CRT display, a liquid crystal display, an organic EL display, and a plasma display, depending on the type of the user terminal 3.

[0045] The input unit 35 may be included in the housing of the user terminal 3 or may be external. For example, the input unit 35 may be integrated with the display unit 34 and implemented as a touch panel. If it is a touch panel, the user can input click operations, slide operations, etc. Of course, a switch button, a mouse, a QWERTY keyboard, etc. may be used instead of a touch panel. That is, the input unit 35 receives operation inputs performed by the user. The input is transmitted to the processor 33 as a command signal via the communication bus 30, and the processor 33 can perform specified control and calculations as needed.

[0046] 2. Functional Structure of Information Processing Device 2

[0047] Figure 4 2 is a diagram showing an example of a functional unit included in the processor 23. Figure 4 As shown, the processor 23 includes an acquisition unit 231 , a data assimilation unit 232 , a correction unit 233 , and an output unit 234 .

[0048] The acquisition unit 231 is configured to acquire information from the user terminal 3 or other devices. The acquisition unit 231 is configured to acquire, for example, a first estimated value as a calculation result of a predetermined physical property simulation, a measured value obtained by measuring a substance, a relational expression representing a function of an object physical property and a field, etc. These are described in detail later.

[0049] The acquisition unit 231 is configured to acquire various information by reading various information stored in a storage area that is at least a part of the storage unit 22 and writing the read information into a working area that is at least a part of the storage unit 22. The storage area is, for example, an area implemented as a storage device such as an SSD in the storage unit 22. The working area is, for example, an area implemented as a memory such as a RAM.

[0050] The data assimilation unit 232 is configured to perform data assimilation of the first estimated value based on the measurement value acquired by the acquisition unit 231. The data assimilation unit 232 is configured to generate a second estimated value by performing the data assimilation. The second estimated value can also be referred to as the first estimated value after data assimilation.

[0051] The correction unit 233 is configured to correct the acquisition result acquired by the acquisition unit 231 and the result of the data assimilation processing performed by the data assimilation unit 232 using various parameters.

[0052] The output unit 234 is configured to be able to output various information such as the first estimated value and the second estimated value. This information can be presented to the user via the display unit 34 of the user terminal 3 or other devices. In this case, for example, the output unit 234 controls the display unit 34 of the user terminal 3 to display visual information such as images, icons, messages, etc. including screens, still images, or dynamic images. The output unit 234 may also generate only rendering information for displaying visual information on the user terminal 3. In addition, the output unit 234 may present the output information to the user without passing through the user terminal 3 or other devices.

[0053] 3. About information processing

[0054] In this section, the information processing performed in the aforementioned information processing system 1 is described. The information processing is used, for example, as follows: using the simulation results of simulating a model of a material having a strongly ordered phase to simulate the dynamic properties of the strongly ordered phase. Below, as an example, an example of information processing with a material having a ferromagnetic phase as a strongly ordered phase (ferromagnet) as the object is described. Examples of ferromagnets include iron-based magnets such as Fe, Fe3O4, FePt, and Ni-Zn ferrite. In addition, the ferromagnet is not limited to this, and is arbitrary, and can be an inorganic compound magnet such as a Co-based magnet, a Ni-based magnet, and a Nd-based magnet, or an organic magnet.

[0055] 3.1. Information processing procedures

[0056] Figure 5 1 is a flowchart showing an example of the flow of information processing executed in the information processing system 1. In addition, the information processing may include any exception processing not shown. The exception processing includes interruption of the information processing and omission of each processing. The selection or input performed in the information processing may be based on the user's operation or may be performed automatically without relying on the user's operation.

[0057] [Step S1]

[0058] First, in step S1, the acquisition unit 231 acquires a model of a material having a ferromagnetic phase, and the processor 23 performs a prescribed physical property simulation based on the acquired model. Thus, the output unit 234 outputs a first estimated value related to the physical property of the ferromagnet. The first estimated value includes the temperature dependence of the order parameter in the strongly ordered phase and the coupling coefficient. The first estimated value may also include the temperature dependence of anisotropy energy, the temperature dependence of the exchange stiffness constant A (exchange stiffness constant), the temperature dependence of the damping constant α, etc.

[0059] <Temperature dependence of order parameter>

[0060] The temperature dependence of the order parameter in the strongly ordered phase may include a saturation value and a phase transition temperature. The saturation value is the value of the order parameter corresponding to the saturation state of the strongly ordered phase at absolute zero. The phase transition temperature represents the phase transition from the strongly ordered phase caused by the value of the order parameter becoming 0. In the present embodiment, the strongly ordered phase is a ferromagnetic phase, so the order parameter is the spontaneous magnetization M of the material. The saturation state is a state in which the object material becomes a magnetic domain structure representing an almost single strong order. The saturation value is the saturation magnetization of the material, in particular, the saturation magnetization M0 at absolute zero. The phase transition temperature is the Curie temperature Tc corresponding to the phase transition from the ferromagnetic phase to the paramagnetic phase. That is, the temperature dependence of the order parameter in the strongly ordered phase is the temperature dependence of the spontaneous magnetization M in the ferromagnetic phase. The spontaneous magnetization M of the present embodiment has a temperature dependence such that the spontaneous magnetization M decreases from the saturation magnetization M0 as the temperature rises, and becomes 0 at the Curie temperature Tc. Hereinafter, for convenience of description, the temperature dependence of the spontaneous magnetization M included in the first estimated value is referred to as the first temperature dependence of the spontaneous magnetization M1 , and the Curie temperature Tc included in the first estimated value is referred to as the first estimated Curie temperature Tc1 .

[0061] <Coupling coefficient>

[0062] The coupling coefficient indicates the magnitude of the interaction between sites of a substance that contributes to the formation of a strongly ordered phase. Accompanying the strongly ordered phase being a ferromagnetic phase, the coupling coefficient of the present embodiment is the magnetic exchange coefficient Jij. The magnetic exchange coefficient Jij indicates the interaction between sites. In detail, the magnetic exchange coefficient Jij indicates the interaction between spins located at the i-th site and the j-th site in a substance. The interaction between spins may include an exchange interaction between spins, or a magnetic interaction between spins, etc. The magnetic exchange coefficient Jij, for example, specifies the first Hamiltonian operator H1 corresponding to the exchange energy between spins.

[0063] Formula (1)

[0064]

[0065] In addition, i and j are indices representing sites within the substance. S_i is the spin operator of the i-th site respectively. The spin operator S_i of this embodiment is represented by the classical Heisenberg model such as Si=(S_ix, S_iy, S_iz). In addition, the model representing the spin system of the substance is not limited to this, and can be appropriately set according to the system to be solved, such as the Ising model, the XY model, etc. In the following, for the sake of convenience of explanation, the magnetic exchange coefficient Jij contained in the first estimated value is referred to as the first estimated magnetic exchange coefficient Jij1.

[0066] <Exchange stiffness constant A>

[0067] The exchange stiffness constant A is a quantity indicating the magnitude of the change in exchange energy per unit volume. The exchange stiffness constant A can be calculated based on the magnetic exchange coefficient Jij. The exchange stiffness constant A0 at absolute zero is expressed, for example, by the sum of the following magnetic exchange coefficients Jij using the mean field approximation.

[0068] Formula (2)

[0069]

[0070] Here, n is the number of atoms contained in the unit cell of the substance to be calculated, and a is the lattice constant of the unit cell.

[0071] In the mean field approximation, the temperature dependence of the exchange rigidity constant A is expressed as follows using the temperature dependence of the exchange rigidity constant A0, the saturation magnetization M0, and the spontaneous magnetization M at absolute zero.

[0072] Formula (3)

[0073]

[0074] <Anisotropy Energy>

[0075] The anisotropy energy indicates the magnitude of the anisotropy of the order parameter of the strongly ordered phase in the material. The anisotropy energy of the present embodiment is the magnetic anisotropy energy K (magnetic anisotropy energy: MAE). The magnetic anisotropy energy K varies depending on the orientation of the spins in the ferromagnet. The magnetic anisotropy energy K may include, for example, contributions from the second Hamiltonian H2 caused by the uniaxial anisotropy of the spins, the third Hamiltonian H3 caused by the symmetry of the crystal structure, and the like. The third Hamiltonian H3 of the present embodiment is the Hamiltonian when the material is a cubic crystal.

[0076] Formula (4)

[0077]

[0078] Formula (5)

[0079]

[0080] In addition, u is an index representing any one of the x, y, and z directions of the coordinates, and e_u represents a unit vector in the direction corresponding to u. k_u and k_c are parameters representing the degree of their respective magnetic anisotropy, and are determined by, for example, the type of atoms, the crystal structure, the distance between sites, etc. Hereinafter, for the sake of convenience, the temperature dependence of the magnetic anisotropy energy K included in the first estimated value is referred to as the temperature dependence of the first estimated magnetic anisotropy energy K1.

[0081] <Model of Matter>

[0082] The model of the substance includes, for example, the Hamiltonian of the object, information related to the crystal structure of the substance, an approximation method of the physical properties in the substance (the type, size, and form of expression of the interactions added to the calculation), etc. The Hamiltonian of the object is appropriately set according to the system of interest. For example, the Hamiltonian of the object may include the contributions of the first Hamiltonian H1 to the third Hamiltonian H3 mentioned above. In addition, the Hamiltonian of the object may also include terms corresponding to the contribution of the Zeeman energy, the contribution of the Galosinski-Moriya interaction, etc.

[0083] Information related to the crystal structure of a substance may include, for example, lattice constant, composition, lattice number, lattice symmetry (space group) and other information related to the lattice, the number of atoms contained in the lattice, position, valence, orbital state, state of electron spin, symmetry (point group) around atoms and other information related to atoms, and other arbitrary information. This information may be information included in any crystal structure database, or information recorded in a paper, or information obtained by various measurements such as X-ray diffraction experiments. The model of a substance includes at least one site for atomic configuration.

[0084] The physical property simulation of this embodiment includes first principle calculation and finite temperature calculation. Physical property simulation may also include micromagnetic simulation, phase field simulation, device simulation, etc.

[0085] <First principles calculation>

[0086] The first principles calculation outputs a first estimate at absolute zero based on the model of the acquired substance. The first principles calculation of this embodiment is performed using density functional theory (DFT). In addition, the calculation method of the first estimate at absolute zero is not limited to the first principles calculation, and any method such as the Hartree-Fock method, mean field approximation, classical Monte Carlo method, quantum Monte Carlo method, and variational Monte Carlo method can be used. The first principles calculation of this embodiment outputs the saturation magnetization M0 at absolute zero, the coupling coefficient (magnetic exchange coefficient Jij), and the magnetic anisotropy energy K at absolute zero as the first estimate at absolute zero. The magnetic anisotropy energy K can include energy caused by uniaxial anisotropy and energy caused by the symmetry of the crystal structure.

[0087] <Finite Temperature Calculation>

[0088] The finite temperature calculation outputs the first estimated value at the finite temperature based on the output first estimated value at absolute zero. The first estimated value at the finite temperature includes the temperature dependence of the spontaneous magnetization M at the finite temperature and the temperature dependence K1 of the magnetic anisotropy energy at the finite temperature. The temperature dependence of the spontaneous magnetization M at the finite temperature includes the Curie temperature Tc at which the spontaneous magnetization M becomes 0. The specific method of the finite temperature calculation is, for example, any method such as the quantum Monte Carlo method, the first principles molecular dynamics method, and the first principles lattice dynamics method. In this embodiment, the classical Monte Carlo method is deliberately used as the finite temperature calculation. Thus, by reducing the calculation load for obtaining the first estimated value, it is possible to achieve a shortening of the calculation time and simulation in a larger system. In the following, for the sake of convenience, the first estimated value at absolute zero and the first estimated value at the finite temperature are sometimes collectively referred to as the first estimated value. In other words, the first estimated value includes the first estimated value at absolute zero and the first estimated value at the finite temperature.

[0089] Furthermore, the physical property simulation may not be performed by the information processing device 2 itself, but may be performed by an external device such as a supercomputer, cloud computing, etc. In this case, the information processing device 2 may perform the calculation indirectly by communicating with the external device.

[0090] [Step S2]

[0091] Next, the process proceeds to step S2 , and the acquisition unit 231 acquires the first estimated value calculated by the physical property simulation and the measured value obtained by measuring the substance that is the subject of the physical property simulation.

[0092] <Measured value>

[0093] The measured value is a physical property value measured in a strongly ordered state. The measured value includes at least a part of the temperature dependence of the spontaneous magnetization M. The temperature dependence of the spontaneous magnetization M includes, for example, the Curie temperature Tc as the phase transition temperature, the saturation magnetization M0 at absolute zero, etc. Hereinafter, for the sake of convenience, the temperature dependence of the spontaneous magnetization M included in the measured value is referred to as the temperature dependence of the measured magnetization ME, the Curie temperature Tc included in the measured value is referred to as the measured Curie temperature TcE, and the saturation magnetization M0 at absolute zero included in the measured value is referred to as the measured saturation magnetization M0E.

[0094] The temperature dependence of the measured magnetization ME may also include the value of the spontaneous magnetization M in the material at a finite temperature other than the phase transition temperature. Specifically, the temperature dependence of the spontaneous magnetization M may also include the value of the spontaneous magnetization M at a finite temperature between absolute zero and the Curie temperature Tc. In addition, the measured value may not include the Curie temperature Tc itself or the saturation magnetization M0 itself, and may also be obtained by fitting the measurement results of the spontaneous magnetization M at multiple temperatures. In addition, the measured saturation magnetization M0E may also be the spontaneous magnetization M measured near absolute zero, or the value obtained by extrapolation of the spontaneous magnetization M. In addition, the measurement of the Curie temperature TcE is not limited to the temperature at which the spontaneous magnetization M completely becomes 0, but may also be a temperature obtained based on the temperature before and after the spontaneous magnetization M becomes 0. Such a temperature dependence of the spontaneous magnetization M can be measured, for example, using a superconducting quantum interference device (SQUID) magnetometer.

[0095] The measured value may include a sensitivity representing the response of the order parameter to a field conjugate to the order parameter. The field conjugate to the order parameter in the present embodiment is a magnetic field (magnetic region). In addition, the sensitivity is magnetic sensitivity, in particular, complex magnetic sensitivity μ. The complex magnetic sensitivity μ is obtained, for example, based on the measurement result of the spontaneous magnetization M under the application of an alternating magnetic field. Such magnetic field dependence of the spontaneous magnetization M can be measured, for example, using the above-mentioned SQUID magnetometer. Hereinafter, for the sake of convenience, the complex magnetic sensitivity μ included in the measured value will be referred to as the measured magnetic sensitivity μE.

[0096] In addition, the measured value may include magnetic anisotropy K. Magnetic anisotropy K represents the difference in free energy when a ferromagnetic body is magnetized along the easy magnetization axis and the hard magnetization axis. Magnetic anisotropy K is obtained, for example, based on the history of magnetic fields relative to magnetization based on the following relationship.

[0097] Formula (6)

[0098]

[0099] M_s represents the saturation magnetization at a certain temperature. H_ext represents the magnetic field. Axis1 represents the easy magnetization axis, and axis2 represents the hard magnetization axis. The magnetic anisotropy energy K can be calculated, for example, based on the saturation magnetization M_s and the magnetic field dependence of the magnetization at each measured temperature. In the following, for the sake of convenience, the temperature dependence of the magnetic anisotropy energy K contained in the second estimated value is referred to as the temperature dependence K2 of the second estimated magnetic anisotropy energy, and the temperature dependence of the magnetic anisotropy energy K contained in the measured value is referred to as the temperature dependence KE of the measured magnetic anisotropy energy. The measured value of the magnetic anisotropy energy K is obtained, for example, by measuring the magnetic field dependence of the spontaneous magnetization M and integrating the hysteresis curve obtained based on the dependence.

[0100] <Damping constant α>

[0101] The damping constant α indicates the degree of attenuation of the microscopic order parameter of the site. The damping constant α of the present embodiment is the Gilbert damping constant used in the following Landaulifshitz-Gilbert equation (LLG equation), and indicates, for example, the degree of suppression of the precession of magnetization by the effective magnetic field H_eff.

[0102] Formula (7)

[0103]

[0104] m is the local magnetization. H_eff is the effective magnetic field acting on the magnetization m. The effective magnetic field H_eff includes, for example, the contribution of the exchange energy of the first Hamiltonian operator H1, the contribution of the anisotropy energy of the second Hamiltonian operator H2, the contribution of the Zeeman effect, the contribution of demagnetization (demagnetizerm), etc. γ is the gyromagnetic constant. The damping constant α can be measured, for example, by ferromagnetic resonance measurement.

[0105] [Step S3]

[0106] Next, the processing enters step S3, and the data assimilation unit 232 performs data assimilation processing based on the acquired first estimated value and measured value. Thus, the data assimilation unit 232 calculates the second estimated value by assimilating the first estimated value into the measured value. The second estimated value may include the same physical property value as the first estimated value. The second estimated value includes, for example, the temperature dependence of the spontaneous magnetization M after data assimilation, the Curie temperature Tc, the magnetic exchange coefficient Jij, the magnetic anisotropy energy K, the exchange stiffness constant A, etc. The details of the data assimilation processing will be described later. In the following, for the sake of convenience, the temperature dependence of the spontaneous magnetization M included in the second estimated value is referred to as the temperature dependence M2 of the second spontaneous magnetization, and the Curie temperature Tc included in the second estimated value is referred to as the second estimated Curie temperature Tc2. In addition, the magnetic exchange coefficient Jij included in the second estimated value is referred to as the second estimated magnetic exchange coefficient Jij2. Furthermore, the temperature dependence of the magnetic anisotropy energy K included in the second estimated value is referred to as the temperature dependence K2 of the second estimated magnetic anisotropy energy.

[0107] [Step S4]

[0108] Next, the process proceeds to step S4, where the output unit 234 outputs the coupling coefficient after the first data assimilation process as a second estimated value. The output second estimated value is used for any purpose, such as an input parameter of a micromagnetic simulation. Specifically, the output unit 234 performs a micromagnetic simulation by substituting the first estimated value and the second estimated value into the LLG equation.

[0109] 3.2. Data assimilation process

[0110] Next, the data assimilation processing in step S3 will be described. Figure 6 It is a flowchart showing the flow of data assimilation processing.

[0111] [Step S100]

[0112] First, in step S100 , the data assimilation unit 232 performs a process including a first data assimilation process on the coupling coefficient and a second data assimilation process on the temperature dependency M1 of the spontaneous magnetization M included in the acquired first estimated value based on the acquired first estimated value and measured value.

[0113] In the first data assimilation process, the data assimilation unit 232 multiplies the ratio of the measured reference value to the estimated reference value by the coupling coefficient included in the acquired first estimated value according to the order indicating the dependency of the estimated reference value on the coupling coefficient. Thus, the data assimilation unit 232 performs data assimilation on the coupling coefficient.

[0114] <Measurement reference value>

[0115] The measurement reference value is a physical property value used in the data assimilation process (particularly the first data assimilation process). The measurement reference value includes at least one of the measured Curie temperature TcE as the phase transition temperature and the measured saturation magnetization MOE as the saturation value.

[0116] <Estimated benchmark value>

[0117] The estimated reference value is a value corresponding to the measured reference value among the acquired first estimated values, the first estimated Curie temperature Tc1 as the phase transition temperature, and the first estimated saturation magnetization M01 as the saturation value. When the measured reference value includes the measured Curie temperature TcE, the estimated reference value includes the first estimated Curie temperature Tc1. On the other hand, when the measured reference value includes the measured saturation magnetization M0E, the estimated reference value includes the first estimated saturation magnetization M01.

[0118] Furthermore, when the measured value includes the measurement reference value, the data assimilation unit 232 performs second data assimilation processing by multiplying the temperature dependency M1 of the spontaneous magnetization M included in the acquired first estimated value based on the ratio of the measurement reference value to the estimated reference value.

[0119] The output unit 234 outputs the second estimated magnetic exchange coefficient Jij2 and the temperature dependence M2 of the second spontaneous magnetization as the result of the processing of step S100. In addition, in the output unit 234 of the present embodiment, as the result of the processing of step S100, the temperature dependence of the exchange stiffness constant A is calculated based on the temperature dependence of the magnetic exchange coefficient Jij and the saturation magnetization M0 included in the estimated value, and is output as the second estimated value.

[0120] [Step S200]

[0121] Next, the process proceeds to step S200, and the data assimilation unit 232 performs a third data assimilation process on the temperature dependence of the anisotropic energy included in the first estimated value (the temperature dependence of the first estimated magnetic anisotropic energy K1 in this embodiment) based on at least one of the coupling coefficient after the first data assimilation process (the second estimated magnetic exchange coefficient Jij2 in this embodiment) and the temperature dependence of the order parameter after the second data assimilation process (the temperature dependence of the second spontaneous magnetization M2 in this embodiment). As a result, the output unit 234 further outputs the temperature dependence of the anisotropic energy after the third data assimilation process (the temperature dependence of the second estimated magnetic anisotropic energy K2 in this embodiment) as the second estimated value.

[0122] [Step S300]

[0123] Next, the process proceeds to step S300, and the processor 23 performs a physical property simulation based on the second estimated value calculated in step S100 and step S200. As a result, the output unit 234 outputs the temperature dependence of the first damping constant α1. Then, the data assimilation unit 232 performs a fourth data assimilation process on the output first damping constant α1. As a result, the second damping constant α2 after data assimilation is obtained as the second estimated value.

[0124] Using the second estimated values ​​output through these processes, micromagnetic simulation and the like are performed.

[0125] 3.3. Details of the process of step S100

[0126] Next, the processing of the above-mentioned step S100 will be described in detail. Figure 7 It is a flowchart showing the details of the process of step S100.

[0127] [Step S101]

[0128] First, in step S101, the processor 23 determines whether the acquired measured value includes the measured Curie temperature TcE. This determination may be made based on the user's input or the form of the measured value.

[0129] [Step S102]

[0130] When the measured value includes the measured Curie temperature TcE (when the determination result of step S101 is affirmative), the process proceeds to step S102, and the data assimilation unit 232 performs data assimilation of the first estimated magnetic exchange coefficient Jij1 based on the measured Curie temperature TcE as the measurement reference value and the first estimated Curie temperature Tc1 as the estimated reference value corresponding to the measured Curie temperature TcE. The process of step S102 of performing data assimilation on the first estimated magnetic exchange coefficient Jij1 can be said to be the first data assimilation process when the estimated reference value is the first estimated Curie temperature Tc1. It can also be said to be the first data assimilation process.

[0131] In detail, the data assimilation unit 232 calculates the ratio TcE / Tc1 of the measured Curie temperature TcE to the first estimated Curie temperature Tc1. Next, the data assimilation unit 232 multiplies the first estimated magnetic exchange coefficient Jij1 by the power of the ratio TcE / Tc1 based on the Curie temperature Tc dependence of the magnetic exchange coefficient Jij, thereby calculating the second estimated magnetic exchange coefficient Jij2. The Curie temperature Tc dependence of the magnetic exchange coefficient Jij, for example, includes the proportional order of the Curie temperature Tc relative to the magnetic exchange coefficient Jij. In the present embodiment, if only the closest interaction is considered, the magnetic exchange coefficient Jij is proportional to the first power of the Curie temperature Tc, so the data assimilation unit 232 calculates the second estimated magnetic exchange coefficient Jij2 by multiplying the first estimated magnetic exchange coefficient Jij1 by the first power of the ratio TcE / Tc1. Therefore, the data assimilation unit 232 performs the first data assimilation, and by substantially replacing the contribution of the first estimated Curie temperature Tc1 contained in the first estimated magnetic exchange coefficient Jij1 with the contribution of the measured Curie temperature TcE, a second estimated magnetic exchange coefficient Jij2 can be obtained which is less inconsistent with experimental facts than the first estimated magnetic exchange coefficient Jij1.

[0132] [Step S103]

[0133] Next, the process proceeds to step S103, and data assimilation of the temperature dependence M1 of the first spontaneous magnetization is performed based on the ratio TcE / Tc1 of the measured Curie temperature TcE to the first estimated Curie temperature Tc1. As a result, the temperature dependence M2 of the second spontaneous magnetization having a Curie temperature Tc that is more consistent with experimental facts than the temperature dependence M1 of the first spontaneous magnetization can be obtained. In this case, when the difference between the second estimated Curie temperature Tc2 re-calculated using the second estimated magnetic exchange coefficient Jij2 and the measured Curie temperature TcE is greater than a certain threshold, the second estimated Curie temperature Tc2 may be used as the first estimated Curie temperature Tc1 again, and the process returns to step S102. It can also be said that step S103 of performing data assimilation of the temperature dependence M1 of the first spontaneous magnetization is one of the second data assimilation processes of this embodiment.

[0134] In this embodiment, the data assimilation unit 232 uses the coupling coefficient (second estimated magnetic exchange coefficient Jij2) after the first data assimilation process to perform finite temperature calculation again (for example, classical Monte Carlo calculation). Thus, when the second estimated magnetic exchange coefficient Jij2 is used for finite temperature calculation, the processing can be simplified compared to the case where a different method of finite temperature calculation is used in the calculation of the first estimated magnetic exchange coefficient Jij1. In addition, the method of finite temperature calculation used in step S103 may also be different from the method of finite temperature calculation used in step S1. The second estimated magnetic exchange coefficient Jij2 is obtained based on the ratio TcE / Tc1, so the processing based on the second estimated magnetic exchange coefficient Jij2 can be said to be processing based on the ratio TcE / Tc1.

[0135] In the next finite temperature calculation, at least a part of the values ​​(such as the first estimated value) calculated by the previous finite temperature calculation may be used as a constraint condition. This limits the calculation range and thus can suppress the divergence of the calculation amount.

[0136] The specific method of data assimilation of the temperature dependence M1 of the first spontaneous magnetization is not limited to this. For example, the data assimilation unit 232 may also transform the temperature as a parameter included in the temperature dependence M1 of the first spontaneous magnetization based on the ratio TcE / Tc1, thereby performing data assimilation of the temperature dependence M1 of the first spontaneous magnetization. Specifically, the data assimilation unit 232 transforms the temperature dependence M1 of the first spontaneous magnetization on the temperature axis. The specific method of this correction is arbitrary, and for example, the conversion of the temperature axis is performed based on the following relationship, for example. In addition, T represents the temperature as a parameter.

[0137] Formula (8)

[0138]

[0139] This conversion is equivalent to a change in the scale of the temperature axis. Therefore, while maintaining the qualitative properties of the temperature dependence M1 of the first spontaneous magnetization, the temperature dependence M2 of the second spontaneous magnetization is obtained in which the first estimated Curie temperature Tc1 is adjusted to the measured Curie temperature TcE.

[0140] Figure 8: is a diagram showing the change in the temperature dependence of the spontaneous magnetization M caused by the data assimilation in step S103. The first estimated Curie temperature Tc1 obtained by the physical property simulation in step S100 is estimated to be greater than the measured Curie temperature TcE. As a result of the processing of the above-mentioned step S103, the temperature dependence M1 of the first spontaneous magnetization is reduced along the temperature axis. Thus, while maintaining the qualitative properties of the temperature dependence M1 of the first spontaneous magnetization, the temperature dependence M2 of the second spontaneous magnetization in which the second estimated Curie temperature Tc2 is consistent with the measured Curie temperature TcE is obtained. In addition, in the processing of step S103, the second estimated saturation magnetization M02 is data assimilated in a manner consistent with the first estimated saturation magnetization M01. Thus, when the measured value is only near the measured Curie temperature TcE, the data assimilation using the measured value can suppress the situation in which the first estimated value in the area not experimentally verified by measurement is affected.

[0141] [Step S104]

[0142] like Figure 7 As shown, next, the process proceeds to step S104, and the processor 23 determines whether the measured value includes the value of the order parameter of the material at a finite temperature other than the phase transition temperature. In this embodiment, the correction unit 233 determines whether the measured value includes the value of the spontaneous magnetization M at a finite temperature other than the measured Curie temperature TcE. In other words, the correction unit 233 determines whether the temperature dependence ME of the measured magnetization includes a value other than the measured reference value (i.e., a value other than the measured Curie temperature TcE or the measured saturation magnetization MOE).

[0143] [Step S105]

[0144] When the measured value includes at least one value of the order parameter of the material at the finite temperature other than the phase transition temperature (when the determination result of step S104 is affirmative), the process proceeds to step S105, and the correction unit 233 further corrects the temperature dependence of the order parameter included in the estimated value based on the value of the order parameter in the material at the finite temperature. In the present embodiment, the correction unit 233 corrects the temperature dependence M2 of the second spontaneous magnetization obtained by the process of step S103 based on the value of the spontaneous magnetization M at the finite temperature included in the temperature dependence ME of the measured magnetization. The specific method of this correction is arbitrary. For example, the correction unit 233 fits the temperature dependence M2 of the second spontaneous magnetization based on the value of the spontaneous magnetization M at the finite temperature by the least square method, the maximum likelihood method, etc., thereby correcting the temperature dependence M2 of the second spontaneous magnetization. At this time, as a constraint condition of this correction, the second estimated Curie temperature Tc2 may also be fixed. Thus, the temperature dependence M2 of the second spontaneous magnetization that is more consistent with the experimental facts can be obtained while keeping the result of the data assimilation of the Curie temperature Tc unchanged through the process of step S103. The correction unit 233 updates the corrected temperature dependence M2 of the second spontaneous magnetization to the latest temperature dependence M2 of the second spontaneous magnetization. The acquisition unit 231 can also obtain the second estimated saturation magnetization M02 based on the value of the temperature dependence M2 of the second spontaneous magnetization at absolute zero.

[0145] [Step S106]

[0146] Next, the processing enters step S106, and the data assimilation unit 232 calculates the temperature dependence of the exchange stiffness constant A based on the temperature dependence of the magnetic exchange coefficient Jij and the spontaneous magnetization M included in the estimated value. The data assimilation unit 232 can calculate the temperature dependence of the exchange stiffness constant A using the above-mentioned relationship. In the case of performing the processing of step S105, the data assimilation unit 232 calculates the temperature dependence of the exchange stiffness constant A based on the second estimated magnetic exchange coefficient Jij2 obtained in step S102 and the temperature dependence M2 of the second spontaneous magnetization corrected in step S105. Then, the output unit 234 outputs the temperature dependence of the exchange stiffness constant A. The output unit 234 outputs the latest values ​​of the various parameters calculated as the second estimated value. In the case of passing through step S105, the second estimated value includes: the second estimated magnetic exchange coefficient Jij2 obtained in step S102, the temperature dependence M2 of the second spontaneous magnetization after correction obtained in step S105, and the temperature dependence of the exchange stiffness constant A obtained in step S106. When the process of step S106 ends, processor 23 ends the process of step S100.

[0147] On the other hand, when the measured value does not include the value of the order parameter in the material at a finite temperature other than the phase transition temperature (when the determination result of step S104 is negative), step S105 is omitted and the processing proceeds to step S106. In this case, the second estimated value includes: the second estimated magnetic exchange coefficient Jij2 obtained in step S102, the temperature dependence M2 of the second spontaneous magnetization obtained in step S103, and the temperature dependence of the exchange rigidity constant A obtained in step S106.

[0148] [Step S107]

[0149] On the other hand, when the measured value does not include the measured Curie temperature TcE (when the determination result in step S101 is negative), the process proceeds to step S107, and the processor 23 determines whether the measured value includes at least one value of the order parameter (spontaneous magnetization M) in the material at a finite temperature other than the phase transition temperature (Curie temperature Tc). The details of the determination process are the same as those of step S104.

[0150] [Step S108]

[0151] When the measured value includes at least one value of the order parameter in the material at a finite temperature (including absolute zero and its vicinity) other than the phase transition temperature (when the determination result of step S107 is affirmative), the process proceeds to step S108, and the correction unit 233 further corrects the temperature dependence of the order parameter included in the estimated value based on the value of the order parameter in the material at the finite temperature. In the present embodiment, the correction unit 233 corrects the temperature dependence M1 of the first spontaneous magnetization acquired by the process of step S1 based on the value of the spontaneous magnetization M at the finite temperature included in the temperature dependence ME of the measured magnetization. Thus, the acquisition unit 231 acquires at least one of the measured Curie temperature TcE and the measured saturation magnetization M0E (i.e., the measurement reference value) from the corrected temperature dependence M1 of the first spontaneous magnetization. The specific method of this correction is arbitrary, for example, the correction unit 233 corrects the temperature dependence M2 of the second spontaneous magnetization by fitting using the least squares method, the maximum likelihood method, etc. In the correction of step S108, the second estimated saturation magnetization M02 is not fixed. Thus, by obtaining the temperature dependence M2 of the second spontaneous magnetization that is more consistent with experimental facts, a more accurate estimated value of the saturation magnetization M0 can be obtained. In the case where the measured saturation magnetization M0E is not experimentally obtained, the correction unit 233 substantially uses the estimated saturation magnetization M0 as the measured saturation magnetization M0E. In the case where the measured saturation magnetization M0E is obtained experimentally, the correction unit 233 directly uses the measured saturation magnetization M0E. The correction unit 233 updates the corrected temperature dependence M1 of the first spontaneous magnetization to the latest temperature dependence M2 of the second spontaneous magnetization. The corrected temperature dependence M1 of the first spontaneous magnetization includes the first estimated saturation magnetization M01 and the first estimated Curie temperature Tc1.

[0152] [Step S109]

[0153] Next, data assimilation is performed on the first estimated magnetic exchange coefficient Jij1 based on the measured saturation magnetization M0E (in other words, the first estimated saturation magnetization M01 after correction) as the measurement reference value and the first estimated saturation magnetization M01 before correction as the estimation reference value. It can also be said that the processing of step S109 of performing data assimilation on the first estimated magnetic exchange coefficient Jij1 is the first data assimilation processing when the estimation reference value is the second estimated saturation magnetization M02.

[0154] In detail, the data assimilation unit 232 calculates the ratio M0E / M01 of the measured saturation magnetization M0E to the first estimated saturation magnetization M01 before correction. Next, the data assimilation unit 232 multiplies the first estimated magnetic exchange coefficient Jij1 by the power of the ratio M0E / M01 based on the spontaneous magnetization M dependence of the magnetic exchange coefficient Jij, thereby calculating the second estimated magnetic exchange coefficient Jij2. The spontaneous magnetization M dependence of the magnetic exchange coefficient Jij includes, for example, the proportional order of the spontaneous magnetization M relative to the magnetic exchange coefficient Jij. In the present embodiment, the magnetic exchange coefficient Jij is proportional to the second power of the spontaneous magnetization M, so the data assimilation unit 232 calculates the second estimated magnetic exchange coefficient Jij2 by multiplying the first estimated magnetic exchange coefficient Jij1 by the square of the ratio M0E / M01. Therefore, the data assimilation unit 232 performs the first data assimilation, substantially replacing the contribution of the temperature dependence M1 of the first spontaneous magnetization contained in the first estimated magnetic exchange coefficient Jij1 with the contribution of the measured saturation magnetization M0E, thereby obtaining a second estimated magnetic exchange coefficient Jij2 that is less inconsistent with experimental facts than the first estimated magnetic exchange coefficient Jij1.

[0155] [Step S110]

[0156] Next, the process proceeds to step S110, and the data assimilation unit 232 performs data assimilation of the temperature dependency M1 of the first spontaneous magnetization after correction based on the ratio M0E / M01 of the measured saturation magnetization M0E to the first estimated saturation magnetization M01 after correction. Thus, the temperature dependency M2 of the second spontaneous magnetization having the saturation magnetization M0 that is more consistent with the experimental facts than the temperature dependency M1 of the first spontaneous magnetization can be obtained. It can also be said that step S110 of performing data assimilation on the temperature dependency M1 of the first spontaneous magnetization is one of the second data assimilation processes of this embodiment.

[0157] In this embodiment, the data assimilation unit 232 uses the coupling coefficient (second estimated magnetic exchange coefficient Jij2) after the first data assimilation process to perform finite temperature calculation (e.g., classical Monte Carlo calculation) again, similarly to step S103. Thus, when the finite temperature calculation is performed using the second estimated magnetic exchange coefficient Jij2, the process can be simplified compared to the case where a different method is used for the finite temperature calculation used to calculate the first estimated magnetic exchange coefficient Jij1.

[0158] Fig. 9This is a diagram showing the change in the temperature dependence of the spontaneous magnetization M caused by the data assimilation in step S110. The temperature dependence M1 of the first spontaneous magnetization obtained by the physical property simulation in step S100 is estimated to be larger than the temperature dependence ME of the measured magnetization. As a result of the processing of the above-mentioned step S110, the divergence between the temperature dependence M1 of the first spontaneous magnetization and the temperature dependence ME of the measured magnetization is suppressed while maintaining the qualitative properties of the temperature dependence M1 of the first spontaneous magnetization. In addition, in the processing of step S110, unlike the processing of step S103, the second estimated saturation magnetization M02 may be different from the first estimated saturation magnetization M01.

[0159] In addition, the specific method of data assimilation of the temperature dependence M1 of the first spontaneous magnetization is not limited to this. For example, the data assimilation unit 232 may also perform data assimilation of the temperature dependence M1 of the first spontaneous magnetization by transforming the temperature as a parameter included in the temperature dependence M1 of the first spontaneous magnetization based on the ratio M0E / M01. Specifically, the data assimilation unit 232 transforms the temperature dependence M1 of the first spontaneous magnetization on the temperature axis. The specific method of this correction is arbitrary, and for example, the conversion of the temperature axis is performed, for example, according to the following relationship. In addition, T represents the temperature as a parameter.

[0160] Formula (9)

[0161]

[0162] This conversion is equivalent to a change in the scale of the temperature axis. Therefore, while maintaining the qualitative properties of the temperature dependence M1 of the first spontaneous magnetization, the temperature dependence M2 of the second spontaneous magnetization is obtained by adjusting the first estimated saturation magnetization M01 to the measured saturation magnetization M0E.

[0163] [Step S106]

[0164] like Figure 7 As shown, the process then proceeds to step S106, and the data assimilation unit 232 calculates the temperature dependence of the exchange stiffness constant A based on the temperature dependence of the magnetic exchange coefficient Jij and the spontaneous magnetization M included in the estimated value. In the case of going through steps S108 to S110, the data assimilation unit 232 calculates the temperature dependence of the exchange stiffness constant A based on the second estimated magnetic exchange coefficient Jij2 obtained in step S109 and the temperature dependence M2 of the second spontaneous magnetization obtained in step S110.

[0165] In addition, when the determination result of step S107 is negative (that is, when the measured value does not include the measured Curie temperature TcE nor the value of the spontaneous magnetization M at a finite temperature other than the measured Curie temperature TcE), steps S108 to S110 are omitted, and the process proceeds to step S106. In this case, the first data assimilation process and the second data assimilation process are omitted, and the processor 23 calculates the exchange stiffness constant A based on the first estimated magnetic exchange coefficient Jij1 acquired in step S2 and the temperature dependence M1 of the first spontaneous magnetization.

[0166] 3.3. Details of the Processing of Step S200

[0167] Next, the processing of the above-mentioned step S200 will be described in detail. Fig.10 It is a flowchart showing the details of the process of step S200.

[0168] [Step S201]

[0169] First, in step S201, the processor 23 determines whether the difference between the coupling coefficient (first estimated magnetic exchange coefficient Jij1) included in the first estimated value and the coupling coefficient (second estimated magnetic exchange coefficient Jij2) after data assimilation processing (specifically, first data assimilation processing) is greater than the first coupling threshold. In addition, the difference between the two can be in any form such as difference, change amount, change rate, ratio, etc. The first coupling threshold can be arbitrarily set according to the accuracy required for the second estimated value.

[0170] [Step S202]

[0171] When the difference between the first estimated magnetic exchange coefficient Jij1 and the second estimated magnetic exchange coefficient Jij2 is greater than the first coupling threshold value (i.e., when the determination result of step S201 is affirmative), the process proceeds to step S202, and the data assimilation unit 232 performs data assimilation on the temperature dependence K1 of the first estimated magnetic anisotropy energy based on at least one of the second estimated values ​​calculated in step S100. The process of step S202 of performing data assimilation on the temperature dependence K1 of the first estimated magnetic anisotropy energy can be regarded as one of the third data assimilation processes of this embodiment.

[0172] In this embodiment, the finite temperature calculation is performed again using the coupling coefficient (second estimated magnetic exchange coefficient Jij2) after the first data assimilation process and the temperature dependence of the order parameter (temperature dependence of the second spontaneous magnetization M2) after the second data assimilation process. As a result, the temperature dependence K2 of the second estimated magnetic anisotropy energy, which reflects information on the magnetic exchange coefficient Jij that is more consistent with experimental facts than the temperature dependence K1 of the first estimated magnetic anisotropy energy, can be obtained. In this embodiment, the method of finite temperature calculation used in step S202 is the same as the method of finite temperature calculation in step S1, but the two may be different.

[0173] In addition, the specific method of data assimilation of the temperature dependence K1 of the first estimated magnetic anisotropy energy is not limited to this. For example, the data assimilation unit 232 can convert the temperature of the parameter included in the temperature dependence K1 of the first estimated magnetic anisotropy energy based on the ratio Tc2 / Tc1 of the second estimated Curie temperature Tc2 to the first estimated Curie temperature Tc1, thereby performing data assimilation of the temperature dependence K1 of the first estimated magnetic anisotropy energy. In detail, the data assimilation unit 232 performs temperature axis transformation on the temperature dependence K1 of the first estimated magnetic anisotropy energy. The specific method of this correction is arbitrary, for example, the temperature axis transformation is performed according to the following relationship, for example.

[0174] Formula (10)

[0175]

[0176] This conversion is equivalent to a change in the scale of the temperature axis. Therefore, while maintaining the qualitative properties of the temperature dependence K1 of the first estimated magnetic anisotropy energy, the second estimated temperature dependence K2 of the magnetic anisotropy energy is obtained, in which the Curie temperature Tc is adjusted to a second estimated Curie temperature Tc2 that reflects experimental facts compared to the first estimated Curie temperature Tc1.

[0177] Fig.11: is a diagram showing the change in the temperature dependence of the magnetic anisotropy energy K caused by the data assimilation in step S202. The temperature dependence K1 of the first estimated magnetic anisotropy energy obtained by the physical property simulation in step S100 is estimated to be larger than the temperature dependence KE of the measured magnetic anisotropy energy. As a result of the processing of step S103, the temperature dependence K1 of the first estimated magnetic anisotropy energy decreases along the temperature axis. Thus, while maintaining the qualitative properties of the temperature dependence K1 of the first estimated magnetic anisotropy energy, the temperature dependence K2 of the second estimated magnetic anisotropy energy is obtained, in which the second estimated Curie temperature Tc2 is consistent with the measured Curie temperature TcE. In addition, in the processing of step S202, the magnetic anisotropy energy K02 at absolute zero contained in the second estimated value is data assimilated in a manner consistent with the magnetic anisotropy energy K01 at absolute zero contained in the first estimated value.

[0178] like Fig.10 As shown, the process then proceeds from step S202 to step S203. In addition, when the difference between the coupling coefficient included in the first estimated value and the coupling coefficient after the first data assimilation process is less than the first coupling threshold (that is, when the determination result of step S201 is negative), the process of step S202 is omitted and the process proceeds to step S203.

[0179] [Step S203]

[0180] Next, in step S203, the processor 23 determines whether the measured value includes the temperature dependency of the anisotropic energy (measures the temperature dependency of the magnetic anisotropic energy KE).

[0181] [Step S204]

[0182] When the measured value includes the temperature dependence of the anisotropic energy (the temperature dependence of the magnetic anisotropic energy KE) (when the determination result of step S203 is positive), the correction unit 233 corrects the temperature dependence of the anisotropic energy included in the estimated value based on the measurement result of the temperature dependence of the anisotropic energy (the temperature dependence of the magnetic anisotropic energy KE). When the process of step S202 is performed, the correction object in step S204 is the temperature dependence K2 of the second estimated magnetic anisotropic energy. On the other hand, when the process of step S202 is omitted, the correction object in step S204 is the temperature dependence K1 of the first estimated magnetic anisotropic energy.

[0183] Here, the correction of the temperature dependence K2 of the second estimated magnetic anisotropy energy will be described in more detail. Fig.12 : is a graph showing the change in the temperature dependence K2 of the second estimated magnetic anisotropy energy caused by the correction in step S204. Fig.12In the figure, the temperature dependence K2 of the second estimated magnetic anisotropy energy before correction in step S203 is marked as K21, and the temperature dependence K2 of the second estimated magnetic anisotropy energy after correction in step S203 is marked as K22.

[0184] The correction unit 233 corrects the temperature dependence K1 of the first estimated magnetic anisotropy energy or the temperature dependence K2 of the second estimated magnetic anisotropy energy based on the value of the magnetic anisotropy energy K at a finite temperature included in the temperature dependence KE of the measured magnetic anisotropy energy. This correction is performed using, for example, the least squares method, the maximum likelihood method, or the like. The temperature dependence K21 of the second estimated magnetic anisotropy energy before correction has already been subjected to data assimilation on the temperature axis in step S202. Therefore, in the correction in step S204, the correction unit 233 fixes the second estimated Curie temperature Tc2 when correcting the temperature dependence K2 of the second estimated magnetic anisotropy energy. Thus, it is possible to maintain consistency with experimental facts. On the other hand, in the correction in step S204, the correction unit 233 does not fix the magnetic anisotropy energy K0 at absolute zero to the magnetic anisotropy energy K01 at absolute zero included in the first estimated value. Thus, it becomes easy to obtain the magnetic anisotropy energy K0 at absolute zero that is more consistent with experimental facts. Fig.12 In the temperature dependence K21 of the second estimated magnetic anisotropy energy before correction and the temperature dependence K22 of the second estimated magnetic anisotropy energy after correction, the second estimated Curie temperature Tc2 is almost the same. On the other hand, as a result of correction based on the temperature dependence KE of the measured magnetic anisotropy energy, the magnetic anisotropy energy K02 at absolute zero after correction is smaller than the magnetic anisotropy energy K01 at absolute zero before correction.

[0185] When the measured value does not include the measured Curie temperature TcE, it is preferable that the magnetic anisotropy energy K01 at absolute zero included in the first estimated value is fixed when correcting the temperature dependency K2 of the second estimated magnetic anisotropy energy. This can suppress the divergence of the calculation amount.

[0186] like Fig.10 As shown, after the processing of step S204 is completed, the processing of step S200 is completed. On the other hand, when the measured value does not include the measurement result of the temperature dependence of the anisotropic energy (the temperature dependence of the magnetic anisotropic energy KE) (when the processing of step S203 is negative), the processing of step S204 is omitted and the processing of step S200 is completed.

[0187] 3.4. Details of the Processing of Step S300

[0188] Next, the processing of the above-mentioned step S300 will be described in detail. Fig.13 It is a flowchart showing the details of the process of step S300.

[0189] [Step S301]

[0190] First, in step S301, the processor 23 determines whether the difference between the first estimated magnetic exchange coefficient Jij1 and the second estimated magnetic exchange coefficient Jij2 is greater than the second coupling threshold. The second coupling threshold can be appropriately set according to the required accuracy and computing resources. In addition, the difference between the first estimated magnetic exchange coefficient Jij1 and the second estimated magnetic exchange coefficient Jij2 is related to the difference between the temperature dependence M1 of the first spontaneous magnetization and the temperature dependence M2 of the second spontaneous magnetization. Therefore, the determination in step S301 is synonymous with the determination based on the difference between the temperature dependence M1 of the first spontaneous magnetization and the temperature dependence M2 of the second spontaneous magnetization.

[0191] [Step S302]

[0192] When the difference between the coupling coefficient (first estimated magnetic exchange coefficient Jij1) contained in the first estimated value and the coupling coefficient (second estimated magnetic exchange coefficient Jij2) contained in the second estimated value is above the second coupling threshold (when the judgment result of step S301 is affirmative), the processing enters step S302, and the processor 23 performs a physical property simulation based on the second estimated value. Specifically, as a physical property simulation, the processor 23 calculates the damping constant α0 at absolute zero based on the first principle calculation. In addition to the damping constant α0, a finite temperature calculation is performed using the second estimated magnetic exchange coefficient Jij2, the second estimated saturation magnetization M02 and other second estimated values ​​as inputs. As a result, the output unit 234 outputs the first damping constant α1. Thus, compared with the case where the first estimated value is used to calculate α1, etc., a damping constant α that is more in line with experimental facts can be obtained. In addition, the specific method of the first principle calculation in step S302 is preferably an algorithm based on the linear response theory contained in the SPR-KKR program. In addition, the specific method of calculation may also use the calculation method of the algorithm in the Akai-KKR program. Then, the process proceeds to step S304.

[0193] Furthermore, the difference between the first estimated magnetic exchange coefficient Jij1 and the second estimated magnetic exchange coefficient Jij2 indicates that the experimental fact differs from the ideal state by an allowable amount or more, and this ideal state is the premise of the simulation in step S1.

[0194] [Step S303]

[0195] On the other hand, when the difference between the coupling coefficient (first estimated magnetic exchange coefficient Jij1) included in the first estimated value and the coupling coefficient (second estimated magnetic exchange coefficient Jij2) after the first data assimilation process is less than the second coupling threshold, the process enters step S303, and the processor 23 performs a physical property simulation based on the first estimated value. Specifically, as a physical property simulation, the processor 23 performs a finite temperature calculation using the first estimated values ​​such as the first estimated magnetic exchange coefficient Jij1 and the first estimated saturation magnetization M01 as inputs. As a result, the output unit 234 outputs the first damping constant α1. Then, the process enters step S304.

[0196] In this way, the calculation of the damping constant α is omitted in the physical property simulation in step S1, and the calculation method of the damping constant α is changed according to the change of the magnetic exchange coefficient Jij caused by data assimilation, thereby saving computing resources for repeated calculations.

[0197] In addition, when the first damping constant α1 and the like are calculated by performing a physical property simulation based on the first estimated value in step S1 , the process of step S303 may be omitted.

[0198] [Step S304]

[0199] In step S304, the processor 23 determines whether the measured value includes information related to the power loss P in the material (ferromagnetic material) caused by applying a field conjugate to the order parameter. In this embodiment, the processor 23 determines whether the measured value includes the measured magnetic sensitivity μE. The power loss P includes, for example, eddy current loss P_E and hysteresis loss P_H. The eddy current loss P_E is represented as follows.

[0200] Formula (11)

[0201]

[0202] V is the volume of the material, d is the thickness of the material, and f is the frequency of the magnetic field H. The data assimilation unit 232 can calculate the resistivity ρ by performing a physical property simulation based on the latest estimated values ​​(temperature dependence M2 of the second spontaneous magnetization, second estimated magnetic exchange coefficient Jij2, temperature dependence K2 of the second estimated magnetic anisotropy energy, etc.). Therefore, the power loss P can be calculated by performing a physical property simulation based on the latest estimated values.

[0203] In addition, the hysteresis loss P_H is expressed as follows.

[0204] Formula (12)

[0205] P H =πVfH 2 μ2(T,H,f)

[0206] μ2 is the imaginary component of the complex magnetic sensitivity μ. The data assimilation unit 232 can calculate the imaginary component μ2 of the complex magnetic sensitivity μ based on the hysteresis loss P_H based on the above relationship. Therefore, the complex magnetic sensitivity μ (especially the imaginary component μ2) can be included in the information related to the power loss P. Hereinafter, for the sake of convenience, the hysteresis loss P_H included in the measured value is referred to as the measured hysteresis loss P_HE. It should be noted that the measured hysteresis loss P_HE is not limited to the actually measured value, but can also be a value calculated based on the measured magnetic sensitivity μE.

[0207] [Step S305]

[0208] Next, the process proceeds to step S305, where the data assimilation unit 232 performs data assimilation on the first damping constant α1 based on the information related to the power loss and the estimated values ​​obtained in steps S100 and S200. Thus, the data assimilation unit 232 calculates the second damping constant α2 as the first damping constant after data assimilation. The process of step S305 of performing data assimilation on the damping constant α can be regarded as one of the fourth data assimilation processes in this embodiment.

[0209] Here, an example of a method of performing data assimilation on the first damping constant α1 in step S305 will be described. Fig.14 It is a diagram showing the details of the processing in step S305.

[0210] First, in step S305, the data assimilation unit 232 sets a plurality of damping constants α for micromagnetic simulation. Specifically, the data assimilation unit 232 sets the damping constant α for micromagnetic simulation based on the first damping constant α. The range of the damping constant α is arbitrary, but it is preferably set to include the first damping constant α1. Fig.14 In FIG. 1 , three values ​​of 0.001, 0.005, and 0.01 are set as the damping constant α as examples.

[0211] Next, the data assimilation unit 232 uses the latest estimated values ​​(temperature dependence M2 of the second spontaneous magnetization, the second estimated magnetic exchange coefficient Jij2, temperature dependence K2 of the second estimated magnetic anisotropy energy, etc.) to perform micromagnetic simulation for each set damping constant α. Thus, the complex magnetic susceptibility μ for each set damping constant α is obtained. At this time, the data assimilation unit 232 can obtain the temperature and magnetic field dependence of the complex magnetic susceptibility μ for each set damping constant α by performing the micromagnetic simulation at multiple temperatures T and frequencies of the magnetic field H.

[0212] The specific method of micromagnetic simulation is arbitrary. The data assimilation unit 232 incorporates the influence of the adjacent sites on the target site as a field effect based on, for example, information related to the structure of the material, the temperature dependence of the spontaneous magnetization M, the temperature dependence of the magnetic anisotropy energy K, the temperature dependence of the exchange stiffness constant A, etc., so as to estimate the size of the effective magnetic field H_eff and the value of the gyromagnetic constant γ. The adjacent sites preferably include at least the closest site, but are not limited to the closest site, and may also include the second closest site, or a site that is farther away from the target site than the second closest site.

[0213] Next, the data assimilation unit 232 calculates the frequency dependency of the hysteresis loss P_H using the complex magnetic susceptibility μ for each damping constant α obtained by the micromagnetic simulation.

[0214] Next, the data assimilation unit 232 compares the calculated hysteresis loss P_H of each damping constant α with the measured hysteresis loss P_HE, and calculates the damping constant α that reproduces the measured hysteresis loss P_HE as the second damping constant α2. The method for determining the second damping constant α2 is arbitrary. For example, the weighted average of the hysteresis loss P_H of each of the multiple damping constants α calculated and the difference between the measured hysteresis loss P_HE are minimized by the least squares method, and the second damping constant α2 is calculated based on the coefficient included in the weighted average. In this way, data assimilation is performed on the damping constant α. In the present embodiment, α2=0.00106. Then, the processing enters step S306.

[0215] If the measured value does not include information on the power loss (if the determination result of step S304 is negative), the process of step S305 is omitted and the process proceeds to step S306 .

[0216] [Step S306]

[0217] In step S306, the output unit 234 outputs various physical property values ​​obtained by the above-mentioned data assimilation processing as the latest estimated values. The output unit 234 outputs the second estimated value for the physical property value that obtains the second estimated value, and outputs the first estimated value for the physical property value that does not obtain the second estimated value. The estimated value includes, for example, the second estimated magnetic exchange coefficient Jij2, the temperature dependence of the second spontaneous magnetization M2, the second damping constant α2, etc. The output estimated value can be used for a predetermined simulation such as a micromagnetic simulation. After that, the processing of step S300 ends.

[0218] By performing the above-described information processing, it is possible to obtain a second estimated value that is less inconsistent with experimental facts than the first estimated value, and a highly accurate simulation result based on the second estimated value.

[0219] 4. Others

[0220] The above-mentioned information processing method is just an example and is not limited to this.

[0221] like Fig.10 As shown, the condition for the data assimilation unit 232 to perform the third data assimilation process (specifically, the process of step S202) is not limited to the difference between the first estimated magnetic exchange coefficient Jij1 and the second estimated magnetic exchange coefficient Jij2 being greater than the first coupling threshold. For example, the data assimilation unit 232 may perform the third data assimilation process when the difference between the first estimated value and the second estimated value of any physical property value that responds to the change in the coupling coefficient, such as the difference between the temperature dependence M1 of the first spontaneous magnetization and the temperature dependence M2 of the second spontaneous magnetization, is greater than a predetermined value.

[0222] For example, in step S201, the processor 23 determines whether the difference between the temperature dependence of the order parameter included in the first estimated value (the temperature dependence of the first estimated spontaneous magnetization M1) and the temperature dependence of the order parameter after the first data assimilation process (the temperature dependence of the second estimated spontaneous magnetization M2) is greater than the first parameter threshold. The difference between the two can be in the form of a difference, a change amount, a change rate, a ratio, etc. The first parameter threshold can be arbitrarily set according to the accuracy required for the second estimated value.

[0223] When the difference between the temperature dependence M1 of the first estimated spontaneous magnetization and the temperature dependence M2 of the second estimated spontaneous magnetization is greater than the first parameter threshold (that is, when the above determination result is affirmative), the data assimilation unit 232 proceeds to step S202, and the data assimilation unit 232 performs data assimilation on the temperature dependence K1 of the first estimated magnetic anisotropy energy based on at least one of the second estimated values.

[0224] If the difference between the temperature dependence M1 of the first estimated spontaneous magnetization and the temperature dependence M2 of the second estimated spontaneous magnetization is smaller than the first parameter threshold (ie, if the determination result is negative), the processing of step S202 is omitted and the processing proceeds to step S203.

[0225] Likewise, if Fig.13 As shown, the condition for the processor 23 to perform the process of step S302 is not limited to the difference between the first estimated magnetic exchange coefficient Jij1 and the second estimated magnetic exchange coefficient Jij2 being greater than the second coupling threshold. For example, the processor 23 may perform a physical property simulation based on the second estimated value in step S302 and output the first damping constant α1 using the output unit 234 when the difference between the first estimated value and the second estimated value of any physical property value that responds to the change in the coupling coefficient, such as the difference between the temperature dependence M1 of the first spontaneous magnetization and the temperature dependence M2 of the second spontaneous magnetization, is greater than a predetermined value.

[0226] For example, the processor 23 determines in step S301 whether the difference between the temperature dependence M1 of the first estimated spontaneous magnetization and the temperature dependence M2 of the second estimated spontaneous magnetization is equal to or greater than a second parameter threshold. The second parameter threshold can be appropriately set according to the required accuracy and computational resources.

[0227] When the difference between the temperature dependence M1 of the first estimated spontaneous magnetization and the temperature dependence M2 of the second estimated spontaneous magnetization is equal to or greater than the second parameter threshold, the process proceeds to step S302, and the processor 23 performs a physical property simulation based on the second estimated value. As a result, the output unit 234 outputs the first damping constant α1.

[0228] On the other hand, when the difference between the temperature dependence M1 of the first spontaneous magnetization and the temperature dependence M2 of the second spontaneous magnetization is smaller than the second parameter threshold, the process proceeds to step S303 .

[0229] The data assimilation process in step S3 does not need to include all of the first data assimilation process and the second data assimilation process in step S100, the third data assimilation process in step S200, and the fourth data assimilation process in step S300. For example, the data assimilation process in step S3 may also include only the first data assimilation process in step S100. In addition, each data assimilation process may also be performed independently.

[0230] The strongly ordered phase to which the information processing is applied is not limited to the ferromagnetic phase. For example, the strongly ordered phase to which the information processing is applied is any of the ferroelectric phase, the strong elastic phase, the strong toroidal phase, etc. In addition, the information processing can be applied to any long-range order in the material as the strong order. As the long-range order, antiferromagnetic phase, weak ferromagnetic phase, tilted antiferromagnetic phase, helical magnetic phase, skyrmion phase, charge ordered phase, etc. can be cited.

[0231] For example, when the strongly ordered phase is a ferroelectric phase, the order parameter is represented by spontaneous polarization or the vibration mode of the atom. The coupling coefficient representing the magnitude of the interaction may include contributions based on Coulomb interaction, overlap integral of electron orbits, spin-orbit interaction, etc., for example. The saturation value is the saturation value of the spontaneous polarization. The phase transition temperature is the Curie temperature representing the phase transition from the ferroelectric phase to the paraelectric phase. The field conjugated to the strongly ordered phase becomes an electric field (electric field). The sensitivity is the electric sensitivity (especially the complex electric sensitivity). The relationship between the above-mentioned physical property values ​​can be obtained using the time-dependent Landau-Lifshitz equation relative to the polarization, linear response theory, Landau phenomenon theory based on symmetry, molecular field theory, etc. The same is true for other strongly ordered phases.

[0232] In step S2, when the measurement value is input to the user terminal 3, the information processing device 2 may acquire the measurement value input to the user terminal 3 from the user terminal 3. In addition, when the information processing device 2 itself functions as a measurement device, the acquisition unit 231 may acquire the measurement result of the information processing device 2 as the measurement value.

[0233] The information processing device 2 may be a local system or a cloud system. The cloud system information processing device 2 may provide the above-mentioned functions and processes in a system such as SaaS (Software as a Service) or cloud computing.

[0234] In the above embodiment, the information processing device 2 performs various storage and control, but a plurality of external devices may be used instead of the information processing device 2. That is, various information and programs may be stored in a plurality of external devices in a distributed manner using blockchain technology or the like.

[0235] The embodiment is not limited to the information processing system 1, and may be an information processing method or an information processing program. The information processing method includes each step of the information processing system 1. The information processing program causes at least one computer to execute each step of the information processing system 1.

[0236] The above-mentioned information processing system 1 and the like can also be provided in the various aspects described below.

[0237] (1) An information processing system comprising at least one processor capable of executing a program to perform the following steps, wherein in an acquisition step, a first estimated value related to a physical property of a substance calculated by a predetermined physical property simulation based on a model of a substance having a strongly ordered phase and a measured value obtained by measuring the substance are acquired, wherein the first estimated value includes: a temperature dependence of an order parameter in the strongly ordered phase; a coupling coefficient representing a magnitude of interaction between sites of the substance that contributes to forming the strongly ordered phase; and in a data assimilation processing step, when the measured value includes a measured reference value, a ratio of the measured reference value to the estimated reference value is calculated based on a value representing a temperature dependence of the order parameter in the strongly ordered phase; The coupling coefficient is multiplied by the number of times of its dependence with the coupling coefficient included in the first estimated value obtained, thereby performing a first data assimilation processing on the coupling coefficient, wherein the measured reference value includes at least one of the following: a phase transition temperature and a saturation value, wherein the phase transition temperature represents the phase transition from the strongly ordered phase when the value of the order parameter becomes 0, and the saturation value is the value of the order parameter corresponding to the saturated state of the strongly ordered phase at absolute zero; the estimated reference value is a value corresponding to the measured reference value among the phase transition temperature and the saturation value included in the first estimated value obtained, and in the output step, the coupling coefficient after the first data assimilation processing is output as a second estimated value.

[0238] According to such a structure, the first estimated value includes information related to the ideal physical properties of the substance as the measurement object. The measurement value includes the quality of the substance, the measurement conditions and other individual inherent information of the substance as the measurement object. Therefore, the second estimated value calculated based on the first estimated value and the measurement value becomes a value that reflects the individual inherent information of the substance relative to the ideal physical properties of the substance. Here, the coupling coefficient represents the strength of the interaction between sites that form a strongly ordered phase. Therefore, it is an important element in determining the properties of the strongly ordered phase, such as the physical property values ​​generated by the strongly ordered phase or the spatial properties formed by the magnetic domains. Therefore, by improving the accuracy of the coupling coefficient through the above-mentioned data assimilation, it is possible to suppress the deviation of the results of the physical property simulation related to the strongly ordered phase from the measurement results of the substance.

[0239] (2) In the information processing system according to (1), in the data assimilation processing step, a second data assimilation processing is performed on the temperature dependence of the order parameter contained in the acquired first estimated value by performing a multiplication operation on the temperature dependence of the acquired order parameter based on the ratio, and in the output step, the temperature dependence of the order parameter after the second data assimilation processing is also output as the second estimated value.

[0240] According to such a structure, it is possible to obtain a temperature dependence of the order parameter that is more consistent with the experimental results than the first estimated value. Here, the size of the order parameter indicates the temperature dependence of the energy required for the phase transition from the strongly ordered phase. Therefore, it is possible to improve the reliability of the temperature design of the device that utilizes the strongly ordered phase.

[0241] (3) In the information processing system described in (2) above, the physical property simulation includes: first principle calculation, which outputs the first estimated value at absolute zero based on the model of the substance; and finite temperature calculation, which outputs the first estimated value at finite temperature based on the first estimated value at absolute zero, wherein the first estimated value at absolute zero includes the coupling coefficient, and in the second data assimilation processing, the finite temperature calculation is performed again using the coupling coefficient after the first data assimilation processing.

[0242] According to such a configuration, information on physical properties at finite temperature is calculated using parameters that match the measurement results, so that a highly reliable estimated value that matches experimental facts can be obtained compared to the case where finite temperature calculation is performed using only the results of first-principles calculation.

[0243] (4) In the information processing method described in (2) or (3) above, in the data assimilation processing step, when the measured value includes the value of the order parameter in the substance at at least one finite temperature other than the phase transition temperature, the temperature dependence of the order parameter included in the estimated value is corrected based on the value of the order parameter in the substance at the finite temperature.

[0244] According to such a structure, the reliability of the temperature dependence of the order parameter at a finite temperature between absolute zero and the phase transition point can be improved.

[0245] (5) In the information processing system according to any one of (2) to (4), the first estimated value also includes the temperature dependence of anisotropic energy indicating the magnitude of the anisotropy of the order parameter. In the data assimilation processing step, when the difference between the coupling coefficient included in the first estimated value and the coupling coefficient after the data assimilation processing is greater than a first coupling threshold, or when the difference between the temperature dependence of the order parameter included in the first estimated value and the temperature dependence of the order parameter after the data assimilation processing is greater than a first parameter threshold, a third data assimilation processing is performed on the temperature dependence of the anisotropic energy included in the first estimated value based on the second estimated value. In the output step, the temperature dependence of the anisotropic energy after the third data assimilation processing is output as the second estimated value.

[0246] According to such a structure, the reliability of the estimation accuracy related to the direction of the order parameter is improved.

[0247] (6) In the information processing system described in (5) above, in the data assimilation processing step, when the measured value includes the temperature dependence of the anisotropic energy, the temperature dependence of the anisotropic energy included in the estimated value is corrected based on the temperature dependence of the anisotropic energy.

[0248] According to such a configuration, the reliability of the estimation accuracy related to the direction of the order parameter is further improved.

[0249] (7) In the information processing system described in any one of (2) to (6) above, in the output step, when the difference between the coupling coefficient included in the first estimated value and the coupling coefficient included in the second estimated value is greater than a second coupling threshold, or when the difference between the temperature dependence of the order parameter included in the first estimated value and the temperature dependence of the order parameter included in the second estimated value is greater than a second parameter threshold, the temperature dependence of the first damping constant is output by performing the physical property simulation based on the second estimated value, where the damping constant represents the degree of attenuation of the microscopic order parameter in the site.

[0250] According to such a structure, the damping constant is one of the factors that determine the relaxation process of the strongly ordered phase. Therefore, by obtaining a damping constant that conforms to experimental facts, the reliability of simulation related to the physical properties of the strongly ordered phase using the damping constant can be improved.

[0251] (8) In the information processing system described in (7) above, in the data assimilation processing step, when the measured value includes information related to power loss in the substance caused by applying a field conjugate to the order parameter, a fourth data assimilation processing is performed on the first damping constant based on the information related to the power loss and the estimated value, and in the output step, the first damping constant subjected to the fourth data assimilation processing is output as the second damping constant.

[0252] According to such a configuration, the damping constant is estimated based on a plurality of experimental facts, so that the estimation accuracy of the damping constant is improved. Therefore, the reliability of the simulation related to the physical properties of the strongly ordered phase using the damping constant can be further improved.

[0253] (9) In the information processing system described in any one of (1) to (8) above, the strongly ordered phase is a ferromagnetic phase, the order parameter is the spontaneous magnetization of the substance, the coupling coefficient is the magnetic coupling coefficient between the sites, the phase transition temperature is the Curie temperature corresponding to the phase transition from the ferromagnetic phase to the paramagnetic phase, and the saturation value is the saturation magnetization of the substance.

[0254] According to such a configuration, information specific to a substance included in a measured value is reflected in the estimated values ​​of various physical property values ​​related to ferromagnetism in particular, thereby improving the convenience of designing a device utilizing the property of ferromagnetism, for example.

[0255] (10) An information processing method comprising the steps of the information processing system according to any one of (1) to (9) above.

[0256] (11) An information processing program that causes at least one computer to execute each step of the information processing system described in any one of (1) to (9) above.

[0257] Of course, it is not limited to this.

[0258] Finally, various embodiments of the present disclosure are described, but these are presented as examples and are not intended to limit the scope of the invention. The new embodiment can be implemented in various other ways, and various omissions, substitutions, and changes can be made without departing from the scope of the invention. The embodiment and its variations are included in the scope and purpose of the invention, and are included in the invention described in the claims and the scope of its equivalent.

[0259] Description of Reference Numerals

[0260] 1: information processing system, 2: information processing device, 3: user terminal, 20: communication bus, 21: communication unit, 22: storage unit, 23: processor, 30: communication bus, 31: communication unit, 32: storage unit, 33: processor, 34: display unit, 35: input unit, 231: acquisition unit, 232: data assimilation unit, 233: correction unit, 234: output unit, A: exchange stiffness constant, A0: exchange stiffness constant, H: magnetic field, Jij: magnetic exchange coefficient, Jij1: first estimated magnetic exchange coefficient, Jij2: second estimated magnetic exchange coefficient, K: magnetic anisotropy energy, K0: magnetic anisotropy energy at absolute zero, K01: magnetic anisotropy energy at absolute zero included in the first estimated value, K02: magnetic anisotropy energy at absolute zero included in the second estimated value, K1: temperature dependence of the first estimated magnetic anisotropy energy, K2: temperature dependence of the second estimated magnetic anisotropy energy , K21: Temperature dependence of the second estimated magnetic anisotropy energy before correction, K22: Temperature dependence of the second estimated magnetic anisotropy energy after correction, KE: Temperature dependence of the measured magnetic anisotropy energy, M: Spontaneous magnetization, M0: Saturation magnetization, M01: First estimated saturation magnetization, M02: Second estimated saturation magnetization, M0E: Measured saturation magnetization, M1: Temperature dependence of the first spontaneous magnetization, M2: Temperature dependence of the second spontaneous magnetization, ME: Temperature dependence of measured magnetization, P: Power loss, P_E: Eddy current loss, P_H: Hysteresis loss, P_HE: Measured hysteresis loss, T: Temperature, Tc: Curie temperature, Tc1: First estimated Curie temperature, Tc2: Second estimated Curie temperature, TcE: Measured Curie temperature, α: Damping constant, α1: First damping constant, α2: Second damping constant, μ: Complex magnetic susceptibility, μ2: Imaginary component, μE: Measured magnetic susceptibility

Claims

1. An information processing system, wherein: The information processing system includes at least one processor capable of executing a program to perform an acquisition step, a data assimilation processing step, and an output step. In the acquisition step, a first estimated value related to a physical property of a substance is acquired, and a measured value is acquired, wherein the first estimated value is calculated by a predetermined physical property simulation based on a model of the substance having a strongly ordered phase, and the measured value is obtained by measuring the substance. The first estimated value includes: the temperature dependence of the order parameter in the strongly ordered phase, and a coupling coefficient, wherein the coupling coefficient represents the magnitude of the interaction between sites of the substance that contributes to the formation of the strongly ordered phase. In the data assimilation processing step, when the measured value includes a measurement reference value, a first data assimilation processing is performed on the coupling coefficient by multiplying the coupling coefficient included in the acquired first estimated value by a ratio of the measurement reference value to the estimated reference value according to a degree representing the dependency of the estimated reference value on the coupling coefficient. The measurement reference value includes at least one of the following: a phase transition temperature indicating a phase transition of the strongly ordered phase caused by the value of the order parameter becoming 0, a saturation value, i.e., a value of the order parameter corresponding to a saturated state of the strongly ordered phase at absolute zero; The estimated reference value is a value corresponding to the measurement reference value among the phase change temperature and the saturation value included in the acquired first estimated value, In the output step, the coupling coefficient subjected to the first data assimilation process is output as a second estimated value.

2. The information processing system according to claim 1, wherein: In the data assimilation processing step, a second data assimilation processing is performed on the temperature dependence of the order parameter contained in the first estimated value obtained by multiplying the temperature dependence of the order parameter obtained based on the ratio, In the output step, the temperature dependence of the order parameter after the second data assimilation process is also output as the second estimated value.

3. The information processing system according to claim 2, wherein: The physical property simulation includes: a first principle calculation, which outputs the first estimated value at absolute zero based on a model of the substance; and a finite temperature calculation, which outputs the first estimated value at a finite temperature based on the first estimated value at absolute zero. wherein the first estimated value at absolute zero includes the coupling coefficient, In the second data assimilation process, the finite temperature calculation is performed again using the coupling coefficient after the first data assimilation process.

4. The information processing system according to claim 2 or 3, wherein: In the data assimilation processing step, when the measured value includes the value of the order parameter in the substance at at least one finite temperature other than the phase transition temperature, the temperature dependence of the order parameter contained in the estimated value is also corrected based on the value of the order parameter in the substance at the finite temperature.

5. The information processing system according to any one of claims 2 to 4, wherein: The first estimate further includes the temperature dependence of anisotropy energy representing the magnitude of anisotropy of the order parameter, In the data assimilation processing step, when the difference between the coupling coefficient included in the first estimated value and the coupling coefficient after the data assimilation processing is greater than a first coupling threshold, or when the difference between the temperature dependence of the order parameter included in the first estimated value and the temperature dependence of the order parameter after the data assimilation processing is greater than a first parameter threshold, a third data assimilation processing is performed on the temperature dependence of the anisotropic energy included in the first estimated value based on the second estimated value. In the output step, the temperature dependence of the anisotropic energy after the third data assimilation process is also output as the second estimated value.

6. The information processing system according to claim 5, wherein: In the data assimilation processing step, when the measured value includes the temperature dependence of the anisotropic energy, the temperature dependence of the anisotropic energy included in the estimated value is corrected based on the temperature dependence of the anisotropic energy.

7. The information processing system according to any one of claims 2 to 6, wherein: In the output step, when the difference between the coupling coefficient included in the first estimated value and the coupling coefficient included in the second estimated value is greater than a second coupling threshold, or when the difference between the temperature dependence of the order parameter included in the first estimated value and the temperature dependence of the order parameter included in the second estimated value is greater than a second parameter threshold, the temperature dependence of the first damping constant is output by performing the physical property simulation based on the second estimated value, The damping constant represents the attenuation degree of the microscopic order parameter in the site.

8. The information processing system according to claim 7, wherein: In the data assimilation processing step, when the measured value includes information related to power loss in the substance caused by applying a field conjugate to the order parameter, a fourth data assimilation processing is performed on the first damping constant based on the information related to the power loss and the estimated value. In the output step, the first damping constant after the fourth data assimilation process is output as a second damping constant.

9. The information processing system according to any one of claims 1 to 8, wherein: The strongly ordered phase is a ferromagnetic phase, The order parameter is the spontaneous magnetization of the substance, The coupling coefficient is the magnetic coupling coefficient between the sites, The phase transition temperature is the Curie temperature corresponding to the phase transition from the ferromagnetic phase to the paramagnetic phase, The saturation value is the saturation magnetization of the substance.

10. An information processing method, comprising the steps of the information processing system according to any one of claims 1 to 9.

11. An information processing program for causing at least one computer to execute each step of the information processing system according to any one of claims 1 to 9.

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