Generation method, simulation method, constraint method and device for magnetic configuration of magnetic material

Abstract

The invention discloses a generation method, a simulation method, a constraint method and a device for a magnetic configuration of a magnetic material, and belongs to the technical field of material science and computers, and the generation method comprises the following steps: obtaining an initial magnetic configuration and an initial atomic configuration of the magnetic material, and randomly generating a plurality of random mechanism type combinations; selecting one of a target magnetic configuration and a target atomic configuration, and obtaining a constraint constant and a wave function of the target magnetic configuration by adopting a self-consistent iteration mode according to the Hamiltonian of the magnetic configuration; determining and storing magnetic system energy, temperature and pressure intensity of the target magnetic configuration; returning to select a new follow-up mechanism type combination, obtaining an initial magnetic configuration and an initial atomic configuration using new different magnetic materials after all the combinations are completed, and repeating the steps; and obtaining the magnetic material with the specified phase transition temperature through the magnetic configuration library by using a deep learning model. According to the method, the target magnetic configuration can be simulated, a magnetic configuration library is formed, and synthesis of a target magnetic material is guided.

Description

Technical Field

[0001] The present invention belongs to the fields of material science and computer technology, and particularly relates to a method for generating, simulating, constraining and a device for the magnetic configuration of a magnetic material. Background Art

[0002] The performance of magnetic materials is usually produced by the interaction of magnetic configuration and atomic configuration. One magnetic configuration often corresponds to multiple possible atomic configurations, because different combinations of atoms or ions, as well as the way they interact with each other, may produce the same magnetic performance. Conversely, if we know the atomic configuration, we cannot directly determine its magnetic configuration, because we also need to consider factors such as the spin and orbital motion of electrons and the superexchange interaction between them.

[0003] Magnetic confinement refers to obtaining a magnetic configuration that deviates from the magnetic ground state in a magnetic material, and using a local potential field to constrain the local distribution of electrons in the magnetic material. The basic principle of magnetic confinement is that electrons with different spin directions have different spatial distributions. However, the current magnetic confinement method has poor confinement effect, and can only constrain the atomic magnetic moment under collinear representation; it can only constrain any variable in the orientation or modulus of the atomic magnetic moment, and cannot accurately constrain both at the same time; the confinement algorithm depends on the initial parameters, lacks adaptability to new systems and new configurations, and some systems are difficult to converge; the confinement is only achieved through electronic iteration under the energy functional, with slow convergence speed and low accuracy; there is no method to integrate with the conventional plane wave first-principles calculation software, and it is impossible to use its high-performance algorithm and high-precision pseudopotential library, which is not easy to use and promote; it is impossible to selectively impose constraints on certain atoms, and it is easy to introduce unreasonable restrictions on non-magnetic atoms. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first purpose of the present application is to form a magnetic configuration library, use the magnetic configuration library to guide the synthesis of magnetic materials through a deep learning model, and improve the existing magnetic constraint method in the process of forming the magnetic configuration library. Specifically, the present application uses a magneto-optical Kerr measurement system to measure the magnetic material and obtain the initial magnetic configuration of the magnetic material, uses an X-ray instrument to diffract the magnetic material to obtain the initial atomic configuration, uses a superconducting quantum interference device to measure the phase transition temperature of a known magnetic material, and uses the phase transition temperature, magnetic configuration, atomic configuration, magnetic system energy, temperature and pressure of the known magnetic material to train a deep learning model, and uses the trained deep learning model to obtain a magnetic material with a specified phase transition temperature through the magnetic configuration library.

[0006] The second purpose of this application is to propose a magnetic configuration simulation method. According to the theoretical initial magnetic configuration, the constraint constants and wave functions of the target magnetic configuration are obtained through the first-principles magnetic constraint method, thereby realizing the simulation analysis of the target magnetic configuration.

[0007] The third purpose of the present application is to propose a first-principles magnetic confinement method and device to solve the technical problem of poor confinement effect of current magnetic confinement methods.

[0008] To achieve the above object, the present application proposes a method for generating a magnetic configuration of a magnetic material, the method comprising:

[0009] Step 1: Use a magneto-optical Kerr measurement system to measure a known magnetic material and obtain a plurality of initial magnetic configurations of the magnetic material, turn off the magneto-optical Kerr measurement system, then use an X-ray instrument to diffract the magnetic material to obtain a plurality of initial atomic configurations, and turn off the X-ray instrument;

[0010] Step 2, randomly generating A random magnetic configurations and B random atomic configurations for each initial magnetic configuration and initial atomic configuration, thereby generating A×B random configuration combinations; wherein A and B are both positive integers;

[0011] Step 3, selecting a random configuration combination in step 2 as the target magnetic configuration and target atomic configuration, and obtaining the constraint constant and wave function of the target magnetic configuration by a self-consistent iteration method according to the Hamiltonian of the magnetic configuration;

[0012] Step 4, determining the magnetic system energy, temperature and pressure of the target magnetic configuration according to the target magnetic configuration, the target atomic configuration, the constraint constant and the wave function of the target magnetic configuration, and storing the target atomic configuration, the target magnetic configuration, the constraint constant, the wave function, the magnetic system energy, the temperature and the pressure of the target magnetic configuration in a magnetic configuration library;

[0013] Step 5, return to step 3, select a new random configuration combination in step 2 as the target magnetic configuration and target atomic configuration and repeat steps 4 to 5 until all random configuration combinations have been selected as the target magnetic configuration and target atomic configuration, then execute step 6;

[0014] Step 6, after the magnetic material is updated, the magneto-optical Kerr measurement system and the X-ray instrument are restarted, and steps 1 to 5 are executed; when the number of magnetic configurations and atomic configurations in the magnetic configuration library reaches a preset value, step 7 is executed;

[0015] Step 7: Use a superconducting quantum interference device to measure the phase transition temperature of a known magnetic material, and use the phase transition temperature, magnetic configuration, atomic configuration, and magnetic system energy, temperature, and pressure of the known magnetic material to train a deep learning model. Use the trained deep learning model to obtain magnetic materials in a specified phase transition temperature range through a magnetic configuration library.

[0016] Preferably, in step 3, a self-consistent iteration method is used to obtain the constraint constants and wave functions of the target magnetic configuration, which specifically includes the following steps:

[0017] Step 331, initialize constraint constant v αa is 0, and the target magnetic moment is obtained according to the target magnetic configuration;

[0018] Step 332, when the constraint constant v is known αa Under the premise of using the Hamiltonian of the initial magnetic configuration to solve the KS equation, the corresponding wave function ψ as the electron density is obtained. The wave function ψ is determined according to the following formula:

[0019] {H KS +H v}||ψ nσ >=∈ n |ψ nσ >

[0020] Among them, H KS is the KS equation, H V is the Hamiltonian of the magnetic system, ψ nσ is the σ component of the spin sub-wave function, ∈ n is the eigenvalue, n is the nth electronic energy level;

[0021] Step 333, according to the wave function ψ and the constraint constant v αa , using the function M(Δv αa ) updates the magnetic moment M of the atom; where,

[0022] Function M(Δv αa ) is determined according to the following formula:

[0023]

[0024] {H KS +H v +ΔH v}|ψ nσ >=∈ n |ψ nσ >

[0025]

[0026] M αa ←∫d 3rm α (r)θ(r cut -|rr a |);

[0027] Among them, H V (r) is the Hamiltonian of the magnetic system, v αa is the constraint constant, is the Pauli matrix in the α direction, θ(·) is the cutoff function, r cut is the cutoff radius, r is the real space coordinate, r a is the real space coordinate of atom a, M αa is the magnetic moment of atom in the α direction; m α (r) is the spin density in the α direction, f n is the occupation number of orbital n, ψ nσ is the σ component of the spin sub-wave function, ψ nσ’ is the σ′ component of the spin sub-wave function; d 3 r is the three-dimensional differential symbol;

[0028] Step 334, determine whether the difference between the magnetic moment of the atom and the target magnetic moment is greater than a set threshold; if it is less than or equal to, execute step 335; if it is greater, update the constraint constant v according to the following formula αa , and return to step 332, using the new constraint constants:

[0029]

[0030] v αa ← αa +Δv αa ;

[0031] in is the target magnetic moment; M is the magnetic moment of the atom, v αa is the constraint constant, For M and Optimize the Lagrangian coefficients;

[0032] Step 335, output the constraint constant v at this time αa and wave function ψ serve as the constraint constant and wave function of the target magnetic configuration.

[0033] Preferably, the specific method for obtaining the energy, temperature and pressure of the magnetic system is:

[0034] The force on the magnetic moment is obtained based on the constraint constant, and the energy of the magnetic system is obtained based on the wave function and the constraint constant or based on the force on the magnetic moment; the temperature and pressure of the magnetic configuration are obtained based on the force on the magnetic moment, the magnetic moment and the atomic configuration.

[0035] Preferably, in step 7, the phase transition temperature, magnetic configuration, atomic configuration, magnetic system energy, temperature and pressure of the known magnetic material are used to train the deep learning model, specifically:

[0036] The magnetic configuration, atomic configuration, magnetic system energy, temperature and pressure of the known magnetic material are taken as input, and the phase transition temperature of the known magnetic material is taken as output to train the deep learning model.

[0037] Preferably, step 7 further comprises: determining the synthesis process of the obtained magnetic material according to the magnetic configuration and phase transition temperature range of the obtained magnetic material.

[0038] The present invention also discloses a method for simulating the magnetic configuration of a magnetic material, comprising the following steps:

[0039] Step 310, obtaining an initial magnetic configuration and an initial atomic configuration;

[0040] Step 320, setting a target magnetic configuration and a target atomic configuration; obtaining a target magnetic moment according to the target magnetic configuration;

[0041] Step 330, according to the Hamiltonian of the magnetic configuration, optimizes the wave function and the constraint constant by a self-consistent iteration method, specifically including the following steps:

[0042] Step 3301, initialize constraint constant v αa is 0, and the target magnetic moment is obtained according to the target magnetic configuration;

[0043] Step 3302, given the known constraint constant v αa Under the premise of using the Hamiltonian of the initial magnetic configuration to solve the KS equation, the corresponding wave function ψ as the electron density is obtained. The wave function ψ is determined according to the following formula:

[0044] {H KS +H v}|ψ nσ >=∈ n |ψ nσ >

[0045] Among them, H KS is the KS equation, H V is the Hamiltonian of the magnetic system, ψ nσ is the σ component of the spin sub-wave function, ∈ n is the eigenvalue, n is the nth electronic energy level;

[0046] Step 3303, according to the wave function ψ and the constraint constant v αa , using the function M(△v αa ) updates the magnetic moment M of the atom; where,

[0047] Function M(△v αa ) is determined according to the following formula:

[0048]

[0049] {H KS +H v +ΔH v}|ψ nσ >=∈ n |ψ nσ >

[0050]

[0051] M αa ←∫d 3 rm α (r)θ(r cut -|rr a |);

[0052] Among them, H V (r) is the Hamiltonian of the magnetic system, v αa is the constraint constant, is the Pauli matrix in the α direction, θ(·) is the cutoff function, r cut is the cutoff radius, r is the real space coordinate, r a is the real space coordinate of atom a, M αa is the magnetic moment of atom in the α direction; m α (r) is the spin density in the α direction, f n is the occupation number of orbital n, ψ nσ is the σ component of the spin sub-wave function, ψ nσ’ is the σ′ component of the spin sub-wave function; d 3 r is the three-dimensional differential symbol;

[0053] Step 3304, determine whether the difference between the atomic magnetic moment and the target magnetic moment is greater than a set threshold; if less than or equal to, execute step 3305; if greater than, update the constraint constant v according to the following formula αa , and return to step 3302, using the new constraint constants:

[0054]

[0055] v αa ←v αa +Δv αa ;

[0056] in is the target magnetic moment; v αa is the constraint constant, For M and Optimize the Lagrangian coefficient, where M is the magnetic moment of the atom;

[0057] Step 3305, output the constraint constant v at this time αa and wave function ψ as the constraint constant and wave function of the target magnetic configuration;

[0058] Step 340, realizing magnetic configuration simulation of magnetic materials:

[0059] Based on the constraint constants and wave functions, the spatial positions of all constrained atoms corresponding to the magnetic configuration, the final magnetic moment of each atom, the magnetic system energy of the constrained magnetic system, the atomic force exerted on each atom, the magnetic interaction force exerted on each atom and the full-degree-of-freedom constraint field are determined to achieve the simulation of the target magnetic configuration.

[0060] Preferably, the step 330 further includes:

[0061] Adding a linear energy contribution to the Hamiltonian, wherein the linear energy contribution includes the constraint constant and the difference between the target magnetic moment and the magnetic moment of the atom, and the dimension of the constraint constant is three times the number of atoms, and the constraint constant of any atom in different directions is different;

[0062] The spatial position of at least one atom in the initial magnetic configuration is fixed, or the magnetic moment of at least one atom in the initial magnetic configuration is fixed, so as to obtain a relaxation process of the magnetic moments of the remaining atoms.

[0063] The present invention also discloses a first-principle magnetic confinement method, which is characterized by comprising:

[0064] Obtaining the magnetic configuration and target magnetic moment of the initial magnetic system; including obtaining the spatial positions of all atoms corresponding to the magnetic configuration of the initial magnetic system and the magnetic moment of each atom;

[0065] Based on the first principles, the electron density and constraint constant of the initial magnetic system are optimized according to the system parameters and the target magnetic moment, so as to minimize the energy of the initial magnetic system; including: optimizing the electron density and constraint constant based on self-consistent iteration; in the process of self-consistent iteration, the change introduced in the situation domain is proportional to the constraint constant, and in the process of optimizing the electron density, the constraint constant remains unchanged; in the process of optimizing the constraint constant, the electron density remains unchanged;

[0066] The magnetic configuration of the constrained magnetic system is determined based on the optimized electron density and the optimized constraint constants. The spatial positions of all constrained atoms, the final magnetic moment of each atom, the system energy of the constrained magnetic system, the atomic force exerted on each atom, the magnetic interaction force exerted on each atom and the full-degree-of-freedom constraint field corresponding to the magnetic configuration of the constrained magnetic system are determined based on the optimized electron density and the optimized constraint constants.

[0067] Preferably, in the process of optimizing the electron density and constraint constant of the initial magnetic system, a linear energy contribution is added to the Hamiltonian, wherein the linear energy contribution includes the constraint constant and the difference between the target magnetic moment and the magnetic moment of the atom. In the linear energy contribution, the dimension of the constraint constant is three times the number of atoms, and the constraint constants of any atom in different directions are different.

[0068] The present invention also discloses a magnetic confinement device for a first-principle magnetic confinement method, comprising:

[0069] An acquisition module, used to acquire the magnetic configuration and target magnetic moment of the initial magnetic system;

[0070] An optimization module, for optimizing the electron density and constraint constant of the initial magnetic system based on the first principles according to the system parameters and the target magnetic moment, so as to minimize the energy of the initial magnetic system;

[0071] A determination module is used to determine the magnetic configuration of the constrained magnetic system based on the optimized electron density and the optimized constraint constant.

[0072] In summary, the technical solution provided by the present invention brings at least the following beneficial effects:

[0073] 1) The present invention forms a magnetic configuration library, and through the analysis and study of existing magnetic materials, the magnetic configuration library can be used to guide the development of target magnetic materials.

[0074] 2) The present invention can obtain a constraint-based target magnetic configuration based on the acquired initial magnetic configuration and atomic configuration, thereby achieving a simulation of the target magnetic configuration.

[0075] 3) The present invention is applicable to atomic magnetic moments under nonlinear representation; and the constraint accuracy is high, which can improve the calculation accuracy of atomic magnetic moments to 10 -8 uB, magnetic energy calculation accuracy increased to 10 -9 eV level.

[0076] 4) The present invention has a wide range of applications. Only a small amount of testing is required to complete the constraint of different magnetic systems. In addition, most parameters are adaptive during the constraint process. Therefore, even very high-energy magnetic excited states can be effectively converged.

[0077] 5) In addition to conventional first-principle calculation outputs, such as final-state magnetic moment and corresponding system energy, the present invention can also provide the constraint field of all degrees of freedom corresponding to the magnetic configuration.

[0078] 6) The present invention can selectively constrain some atoms or some magnetic moment components, and is used to study the relaxation process of other atomic magnetic moments in the process of fixing certain atomic magnetic moments.

[0079] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0081] Figure 1 A flow chart of a first-principles magnetic confinement method provided in an embodiment of the present application;

[0082] Figure 2 A schematic diagram of the distribution of energy in the material NiO provided in the embodiment of the present application along the direction of the magnetic moment of the first Ni atom;

[0083] Figure 3 A schematic diagram of the distribution of energy in the material NiO provided in the embodiment of the present application along with the direction of the magnetic moment of the second Ni atom;

[0084] Figure 4 A schematic structural diagram of a first-principle magnetic confinement device provided in an embodiment of the present application;

[0085] Figure 5 A flow chart of a method for generating a magnetic configuration of a magnetic material provided in an embodiment of the present application. DETAILED DESCRIPTION

[0086] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be construed as limiting the present application. On the contrary, the embodiments of the present application include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0087] The present application is described in detail below with reference to specific embodiments.

[0088] Figure 1 A flow chart of a first-principles magnetic confinement method provided in an embodiment of the present application.

[0089] like Figure 1 As shown, a first-principle magnetic confinement method provided in an embodiment of the present application comprises the following steps:

[0090] Step 110, obtaining the magnetic configuration and target magnetic moment of the initial magnetic system.

[0091] Step 120 , based on first principles, the electron density and constraint constant of the initial magnetic system are optimized according to the system parameters and the target magnetic moment, so as to minimize the energy of the initial magnetic system.

[0092] Step 130 , determining the magnetic configuration of the constrained magnetic system based on the optimized electron density and the optimized constraint constant.

[0093] In the embodiment of the present application, obtaining the magnetic configuration of the initial magnetic system includes:

[0094] Obtain the spatial positions of all atoms corresponding to the magnetic configuration of the initial magnetic system, as well as the magnetic moment of each atom.

[0095] In the embodiment of the present application, the electron density and the constraint constant of the initial magnetic system are optimized according to the system parameters and the target magnetic moment, including:

[0096] The electron density and constraint constants are optimized based on self-consistent iteration.

[0097] During the self-consistent iteration process, the amount of change introduced into the situation domain is proportional to the constraint constant.

[0098] In the embodiment of the present application, the electron density and the constraint constant are optimized based on self-consistent iteration, including:

[0099] During the optimization of the electron density, the constraint constants remain constant.

[0100] During the optimization of the constraint constants, the electron density was kept constant.

[0101] According to some embodiments, the process of optimizing the electron density and the constraint constant specifically includes the following steps:

[0102] Step 210, initializing constraint constant v αa is 0; determine the target magnetic moment.

[0103] Step 220, solving the KS equation according to the Hamiltonian of the system to obtain the corresponding wave function ψ, and specifically determining the wave function ψ according to the following formula:

[0104] {H KS +H v}|ψ nσ >=∈ n |ψ nσ >

[0105] Among them, H KS is the KS equation, H V is the Hamiltonian of the magnetic system, ψ nσ is the wave function, ∈ n is the eigenvalue.

[0106] Step 230, fix the wave function ψ and use the function M(△v αa ) at a fixed constraint constant v αa The atomic magnetic moment M is updated under the condition of ; wherein the function M(△v αa ):

[0107] Function M(△v αa ) is determined according to the following formula:

[0108]

[0109] {H KS +H v +ΔH v}|ψ nσ >=∈ n |ψ nσ >

[0110]

[0111] M αa ←∫d 3 rm α (r)θ(r cut -|rr a |);

[0112] Among them, H V (r) is the Hamiltonian of the magnetic system, v αa is the constraint constant, is the Pauli matrix in the α direction, θ(·) is the cutoff function, r cut is the cutoff radius, r is the real space coordinate, r a is the real space coordinate of atom a, M αa is the magnetic moment of atom in the α direction; m α (r) is the spin density in the α direction, f n is the occupation number of orbital n, ψnσ is the σ component of the spin sub-wave function, ψ nσ’ is the σ′ component of the spin sub-wave function; d 3 r is the three-dimensional differential symbol.

[0113] Step 240, determine whether the difference between the atomic magnetic moment and the target magnetic moment is greater than a set threshold; if not, output the atomic magnetic moment and the constraint constant at this time; if greater, update v according to the following formula αa , and repeat step 230:

[0114]

[0115] v αa ←v αa +Δν αa ;

[0116] in is the target magnetic moment; v αa is the constraint constant, For M and The Lagrangian coefficient is optimized, where M is the magnetic moment of the atom.

[0117] In the embodiment of the present application, the electron density and the constraint constant of the initial magnetic system are optimized according to the system parameters and the target magnetic moment, including:

[0118] During the optimization of the electron density and the constraint constants, a linear energy contribution is added to the Hamiltonian, which includes the constraint constants and the difference between the target magnetic moment and the magnetic moment of the atom.

[0119] According to some embodiments, the Hamiltonian is a direct description of the system's interactions, derived by reapplying variational methods to an energy functional with a magnetic penalty term, first obtained in first-principles density functional theory (DFT).

[0120] In some embodiments, the system energy of the magnetic system is obtained secondarily in DFT and can be calculated by using Harris-Foulkes functional calculations, and double-counting items need to be calculated during the calculation process.

[0121] In the embodiment of the present application, in the process of optimizing the electron density and the constraint constant, a linear energy contribution is added to the Hamiltonian, including:

[0122] In the linear energy contribution, the dimension of the constraint constant is three times the number of atoms, and the constraint constant of any atom in different directions is different.

[0123] In the embodiment of the present application, the electron density and the constraint constant of the initial magnetic system are optimized according to the system parameters and the target magnetic moment, including:

[0124] In the process of optimizing the electron density and constraint constant of the initial magnetic system, the spatial position of at least one atom in the initial magnetic system is fixed, or the magnetic moment of at least one atom in the initial magnetic system is fixed to obtain the relaxation process of the magnetic moments of the remaining atoms.

[0125] In the embodiment of the present application, the magnetic configuration of the constrained magnetic system is determined based on the optimized electron density and the optimized constraint constant, including:

[0126] Based on the optimized electron density and the optimized constraint constants, the spatial positions of all constrained atoms corresponding to the magnetic configuration of the constrained magnetic system, the final magnetic moment of each atom, the system energy of the constrained magnetic system, the atomic force exerted on each atom, the magnetic interaction force exerted on each atom and the full-degree-of-freedom constraint field are determined.

[0127] According to some embodiments, the magnetic interaction force on each atom is obtained by the following two methods:

[0128] The first method is to obtain the differential of the system energy of the magnetic system with respect to the magnetic moment, which is obtained according to the following formula:

[0129]

[0130] Among them, Eλ is the system energy, λ is the constraint constant, and ψλ is the wave function in the system under the constraint conditions.

[0131] The second method is to obtain the magnetic interaction force on each atom, that is, the constraint constant of each atom, which is obtained according to the following formula.

[0132]

[0133] in, is the target magnetic moment, is the energy of the system when the magnetic moment of atom a is constrained in the α direction, E KS [p] is the system energy obtained by solving the KS equation when the electron density is ρ, M αa [p] is the magnetic moment of atom ρ in the initial magnetic system, V αa is the constraint constant.

[0134] In the embodiment of the present application, after determining the magnetic configuration of the constrained magnetic system based on the optimized electron density and the optimized constraint constant, the method further includes:

[0135] High-throughput calculations are performed on the spatial positions of all constrained atoms, the system energy of the constrained magnetic system, the atomic forces acting on each atom, and the magnetic interaction forces acting on each atom to obtain a machine learning data set.

[0136] According to some embodiments, high-throughput computing refers to the process of sampling a large number of atomic positions and target magnetic moments in a certain space to obtain large-scale inputs, using modern computing clusters and task distribution systems to quickly deploy computing, and obtain large-scale outputs of energy, atomic forces, and magnetic interactions.

[0137] In some embodiments, the machine learning data set obtained during high-throughput computing includes the spatial position of atoms, the magnetic moment of atoms, the system energy of the magnetic system, the atomic force exerted on each atom, and the magnetic interaction force exerted on each atom.

[0138] According to some embodiments, the method proposed in the embodiments of the present application can be integrated with the commonly used plane wave DFT software VASP, and its source code can be patched and compiled in the same way; and, it is equipped with corresponding examples and setting templates, which is easy to use, and the parameter settings are compatible with VASP, and do not interfere with any functions of the original VASP.

[0139] Taking a scenario as an example, the method proposed in the embodiment of the present application is used to calculate the distribution of energy in the material Nio along the directions of the magnetic moments of two Ni atoms, such as Figure 2 and Figure 3 As shown, dark colors represent low energy and light colors represent high energy. It can be seen that the method provided in the embodiment of the present application has high constraint accuracy.

[0140] In summary, the method proposed in the embodiment of the present application obtains the magnetic configuration and target magnetic moment of the initial magnetic system; based on the first principle, the electron density and constraint constant of the initial magnetic system are optimized according to the system parameters and the target magnetic moment, thereby minimizing the energy of the initial magnetic system; the magnetic configuration of the constrained magnetic system is determined based on the optimized electron density and the optimized constraint constant. Therefore, it can be applied to the atomic magnetic moment under nonlinear representation; and the constraint accuracy is high, which can improve the calculation accuracy of the atomic magnetic moment to 10 -8 uB, magnetic energy calculation accuracy increased to 10 -9 eV level; wide range of applications, only a small number of tests are needed to complete the constraint for different magnetic systems, and most parameters are adaptive during the constraint process, so even very high-energy magnetic excited states can be effectively converged; in addition to conventional first-principle calculation outputs, such as the final-state magnetic moment and the corresponding system energy, the constraint field of the full degree of freedom corresponding to the magnetic configuration can also be given; selective constraints can be performed on some atoms or some magnetic moment components, which is used to study the relaxation process of other atomic magnetic moments in the process of fixing the magnetic moments of certain atoms.

[0141] In order to implement the above embodiments, the present application also proposes a first-principles magnetic confinement device.

[0142] Figure 4 A schematic structural diagram of a first-principles magnetic confinement device provided in an embodiment of the present application.

[0143] like Figure 4 As shown, a first principle magnetic confinement device 400 comprises:

[0144] The acquisition module 410 is used to acquire the magnetic configuration and target magnetic moment of the initial magnetic system.

[0145] The optimization module 420 is used to optimize the electron density and constraint constant of the initial magnetic system based on the first principles according to the system parameters and the target magnetic moment, so as to minimize the energy of the initial magnetic system.

[0146] The determination module 430 is used to determine the magnetic configuration of the constrained magnetic system based on the optimized electron density and the optimized constraint constant.

[0147] The device proposed in the embodiment of the present application obtains the magnetic configuration and target magnetic moment of the initial magnetic system through the acquisition module; the optimization module optimizes the electron density and constraint constant of the initial magnetic system based on the first principle according to the system parameters and the target magnetic moment, thereby minimizing the energy of the initial magnetic system; the determination module determines the magnetic configuration of the constrained magnetic system based on the optimized electron density and the optimized constraint constant. Therefore, the present application has high constraint accuracy and can significantly improve the constraint effect of the magnetic system.

[0148] The present invention also proposes a method for generating a magnetic configuration of a magnetic material, the method being as follows: Figure 5 As shown, the following steps are included:

[0149] Step 1: Use a magneto-optical Kerr measurement system (MOKE) to measure the magnetic material to obtain the initial magnetic configuration of the magnetic material, turn off the magneto-optical Kerr measurement system, then use X-rays to diffract the magnetic material to obtain the initial atomic configuration, and turn off the X-rays.

[0150] For a magnetic material, a variety of initial magnetic configurations and initial atomic configurations can be obtained, the magnetic moment is obtained according to the magnetic configuration, and the position of each atom in the magnetic configuration is obtained according to the atomic configuration.

[0151] Step 2, randomly generating A random magnetic configurations and B random atomic configurations for each initial magnetic configuration and initial atomic configuration, thereby generating A×B random configuration combinations; wherein A and B are both positive integers.

[0152] The values ​​of A and B can be the same or different; usually the value range of A and B is [50,200]; each magnetic material can contain multiple magnetic configurations, and each magnetic configuration can correspond to multiple atomic configurations. The Monte Carlo method is preferably used for random generation.

[0153] Step 3, selecting a random configuration combination in step 2 as the target magnetic configuration and target atomic configuration, and obtaining the constraint constant and wave function of the target magnetic configuration by a self-consistent iteration method based on the first principles and the Hamiltonian of the magnetic configuration; comprising the following steps:

[0154] Step 331, initialize constraint constant v αa is 0, and the target magnetic moment is obtained according to the target magnetic configuration.

[0155] Step 332, when the constraint constant v is known αa Under the premise of using the Hamiltonian of the initial magnetic configuration to solve the KS equation, the corresponding wave function ψ as the electron density is obtained. The wave function ψ is determined according to the following formula:

[0156] {H KS +H v}|ψ nσ >=∈ n |ψ nσ >

[0157] Among them, H KS is the KS equation, H V is the Hamiltonian of the magnetic system, ψ nσ is the σ component of the spin sub-wave function, ∈ n is the eigenvalue, n is the nth electronic energy level;

[0158] Step 333, according to the wave function ψ and the constraint constant v αa , using the function M(△v αa ) updates the magnetic moment M of the atom; where,

[0159] Function M(△v αa ) is determined according to the following formula:

[0160]

[0161] {H KS +H v +ΔH v}|ψ nσ >=∈ n |ψ nσ >

[0162]

[0163] M αa←∫d 3 rm α (r)θ(r cut -|r-ra|);

[0164] Among them, H V (r) is the Hamiltonian of the magnetic system, v αa is the constraint constant, is the Pauli matrix in the α direction, θ(·) is the cutoff function, r cut is the cutoff radius, r is the real space coordinate, r a is the real space coordinate of atom a, M αa is the magnetic moment of atom in the α direction; m α (r) is the spin density in the α direction, f n is the occupation number of orbital n, ψ nσ is the σ component of the spin sub-wave function, ψ nσ’ is the σ′ component of the spin sub-wave function; d3r is the three-dimensional differential symbol.

[0165] Step 334, determine whether the difference between the atomic magnetic moment and the target magnetic moment is greater than a set threshold; if the difference between the atomic magnetic moment and the target magnetic moment is not greater than the set threshold, execute step 335; if it is greater, update the constraint constant vαa according to the following formula, and return to step 332 to use the new constraint constant:

[0166]

[0167] v αa ←v αa +Δv αa ;

[0168] in is the target magnetic moment; vαa is the constraint constant, For M and The Lagrangian coefficient is optimized, where M is the magnetic moment of the atom. In a preferred embodiment, the threshold value can be set to 10 -4 Bohr magneton.

[0169] In the above calculation, a linear energy contribution is added to the Hamiltonian. The linear energy contribution includes the constraint constant and the difference between the target magnetic moment and the magnetic moment of the atom. The dimension of the constraint constant in the linear energy contribution is three times the number of atoms. The constraint constant of any atom in different directions is different. The change introduced in the situation domain is proportional to the constraint constant.

[0170] Step 335, output the constraint constant v at this time αa and wave function ψ; the constraint constants and wave functions at this time are the constraint constants and wave functions of the target magnetic configuration.

[0171] Step 4, determine the magnetic system energy, temperature and pressure of the target magnetic configuration according to the target magnetic configuration, target atomic configuration, constraint constants and wave functions of the target magnetic configuration, and store the target atomic configuration, target magnetic configuration, constraint constants, wave functions, magnetic system energy, temperature and pressure of the target magnetic configuration in the magnetic configuration library. It should be noted that the temperature and pressure here refer to the temperature and pressure at which the target magnetic configuration can exist.

[0172] The force on the magnetic moment is obtained based on the constraint constant, and the force on the magnetic moment is also called the magnetic interaction force on the atom; the energy of the magnetic system is obtained based on the wave function and the constraint constant, or the energy of the magnetic system is obtained based on the force on the magnetic moment; the temperature and pressure are obtained based on the force on the magnetic moment, the magnetic moment and the atomic configuration.

[0173] Step 5, return to step 3, select a new random configuration combination in step 2 as the target magnetic configuration and target atomic configuration, until all random configuration combinations have been selected as the target magnetic configuration and target atomic configuration, then execute step 6, where the new random configuration combination means a random configuration combination that has not been used in step 3 before.

[0174] Step 6, after updating the magnetic material, restart the magneto-optical Kerr measurement system and the X-ray instrument, and execute steps 1 to 5; when the number of magnetic configurations and atomic configurations in the magnetic configuration library reaches a preset value, execute step 7. In a preferred embodiment, the preset value can be 2000.

[0175] Step 7, use a superconducting quantum interference device such as SQUID to measure the phase transition temperature of known, existing magnetic materials, and use the phase transition temperature, magnetic configuration, atomic configuration, magnetic system energy, temperature and pressure of the known magnetic materials to train the deep learning model, and use the trained deep learning model to obtain magnetic materials in the specified phase transition temperature range through the magnetic configuration library. The synthesis process can be determined based on the magnetic configuration and phase transition temperature of the magnetic material. In a preferred embodiment, the specified phase transition temperature range can be, for example, 600-700 0 C.

[0176] When training the deep learning model, the known magnetic material magnetic configuration, atomic configuration, magnetic system energy, temperature and pressure are used as input, and the phase transition temperature is used as output to train the deep learning model.

[0177] In another aspect of the present invention, a method for simulating the magnetic configuration of a magnetic material is disclosed, the method comprising the following steps:

[0178] Step 310, obtaining an initial magnetic configuration and an initial atomic configuration;

[0179] The initial magnetic configuration is in an ideal state environment of absolute 0 degrees, 0 pressure and 0 magnetic field. The magnetic moment is obtained according to the magnetic configuration, and the position of each atom in the magnetic configuration is obtained according to the atomic configuration.

[0180] Step 320, setting a target magnetic configuration and a target atomic configuration; and obtaining a target magnetic moment according to the target magnetic configuration.

[0181] Step 330, according to the Hamiltonian of the magnetic configuration, the electron density and the constraint constant are optimized by a self-consistent iteration method, which specifically includes the following steps:

[0182] Step 3301, initialize constraint constant v αa is 0, and the target magnetic moment is obtained according to the target magnetic configuration.

[0183] Step 3302, given the known constraint constant v αa Under the premise of using the Hamiltonian of the initial magnetic configuration to solve the KS equation, the corresponding wave function ψ as the electron density is obtained. The wave function ψ is determined according to the following formula:

[0184] {H KS +H v}|ψ nσ >=∈ n |ψ nσ >

[0185] Among them, H KS is the KS equation, H V is the Hamiltonian of the magnetic system, ψ nσ is the σ component of the spin sub-wave function, ∈ n is the eigenvalue, and n is the nth electronic energy level.

[0186] Step 3303, according to the wave function ψ and the constraint constant v αa , using the function M(△v αa ) updates the magnetic moment M of the atom; where,

[0187] Function M(△v αa ) is determined according to the following formula:

[0188]

[0189] {H KS +H v +ΔH v}|ψ nσ >=∈ n |ψ nσ >

[0190]

[0191] M αa←∫d 3 rm α (r)θ(r cut -|rr a |);

[0192] Among them, H V (r) is the Hamiltonian of the magnetic system, v αa is the constraint constant, is the Pauli matrix in the α direction, θ(·) is the cutoff function, r cut is the cutoff radius, r is the real space coordinate, r a is the real space coordinate of atom a, M αa is the magnetic moment of atom in the α direction; m α (r) is the spin density in the α direction, f n is the occupation number of orbital n, ψ nσ is the σ component of the spin sub-wave function, ψ nσ’ is the σ′ component of the spin sub-wave function; d 3 r is the three-dimensional differential symbol.

[0193] Step 3304, determine whether the difference between the magnetic moment of the atom and the target magnetic moment is greater than a set threshold; if not, execute step 335; if greater, update the constraint constant v according to the following formula αa , and return to step 332, using the new constraint constants:

[0194]

[0195] v αa ←v αa +Δv αa ;

[0196] in is the target magnetic moment; v αa is the constraint constant, For M and The Lagrangian coefficient is optimized, where M is the magnetic moment of the atom.

[0197] Preferably, a linear energy contribution is added to the Hamiltonian, the linear energy contribution including a constraint constant and a difference between the target magnetic moment and the magnetic moment of the atom, and the dimension of the constraint constant is three times the number of atoms, and the constraint constants of any atom in different directions are different. The spatial position of at least one atom in the initial magnetic configuration is fixed, or the magnetic moment of at least one atom in the initial magnetic configuration is fixed to obtain a relaxation process of the magnetic moment of the remaining atoms, wherein the remaining atoms refer to atoms other than the fixed atoms in the initial magnetic configuration.

[0198] Step 3305, output the constraint constant v at this time αaand wave function ψ; the constraint constants and wave functions at this time are the constraint constants and wave functions of the target magnetic configuration.

[0199] Step 340, realizing magnetic configuration simulation of magnetic materials.

[0200] Based on the constraint constants and wave functions, the spatial positions of all constrained atoms corresponding to the magnetic configuration, the final magnetic moment of each atom, the magnetic system energy of the constrained magnetic system, the atomic force exerted on each atom, the magnetic interaction force exerted on each atom and the full-degree-of-freedom constraint field are determined to achieve the simulation of the target magnetic configuration.

[0201] The self-consistent iteration method adopted by the present invention optimizes the electron density and the constraint constant, so as to take the size and direction of the atomic magnetic moment as free variables, adaptively adjust the constraint parameters, and realize efficient and accurate calculation of the system energy and wave function. The self-consistent iteration method can provide information about the spin-lattice interaction, which helps to guide the preparation of the magnetically excited state of the material. In addition, it can also be used to calculate the electronic structure of a specific magnetically excited state or even a spin fluctuation configuration, which affects the orbital characteristics and topological characteristics of the band structure and the Fermi surface, and can be used to study basic physical problems such as the interaction between spins and electrons, and guide the rich functional design in terms of dynamics or optical properties.

[0202] Using the method proposed in the present invention, the energy path and kinetic behavior of the AFM-FM phase transition of the antiferromagnetic NiO block under a uniform external field are studied, and it is found that the magnetic moment of the Ni atom has the power of tangential rotation in the direction of the external field and radial stretching along the external field at the same time, and under a strong external field, the latter trend is much stronger than the former. In addition, it is found that the potential barrier for the phase transition of single-crystal NiO restricted by periodicity is significantly higher, indicating the magnetic domain mechanism in its actual transformation process. In the spin-lattice dynamics potential function model based on deep learning, the magnetic configuration generated by this method is used as the generation end of the high-throughput self-consistent data set, which can greatly improve the training speed. The energy prediction of the NiO magnetic excited state using the spin-lattice dynamics potential function model based on deep learning shows that the model has high prediction accuracy, can simulate the spin-lattice dynamics of large-scale systems, and has good extrapolation ability.

[0203] It should be understood that various parts of the present application can be implemented using hardware, software, firmware or a combination thereof. In particular, the data of the present application can be obtained using a variety of devices, and the magnetic configuration obtained by the method of the present application can not only be used to study existing magnetic materials with the aid of a variety of instruments, but can also guide the synthesis of new magnetic materials with better phase transition temperatures in fields such as magnetic materials for electric vehicle engines, magnetic materials for robot motors, and magnetic materials for computer hard drives.

[0204] Moreover, in the above-mentioned embodiments, a plurality of steps or methods may be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it may be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0205] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0206] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0207] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0208] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0209] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0210] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for generating a magnetic configuration of a magnetic material, characterized in that: It includes the following steps: Step 1: Use a magneto-optical Kerr measurement system to measure a known magnetic material and obtain a plurality of initial magnetic configurations of the magnetic material, turn off the magneto-optical Kerr measurement system, then use an X-ray instrument to diffract the magnetic material to obtain a plurality of initial atomic configurations, and turn off the X-ray instrument; Step 2, randomly generating A random magnetic configurations and B random atomic configurations for each initial magnetic configuration and initial atomic configuration, thereby generating A×B random configuration combinations; wherein A and B are both positive integers; Step 3, selecting a random configuration combination in step 2 as the target magnetic configuration and target atomic configuration, and obtaining the constraint constant and wave function of the target magnetic configuration by a self-consistent iteration method according to the Hamiltonian of the magnetic configuration; Step 4, determining the magnetic system energy, temperature and pressure of the target magnetic configuration according to the target magnetic configuration, the target atomic configuration, the constraint constant and the wave function of the target magnetic configuration, and storing the target atomic configuration, the target magnetic configuration, the constraint constant, the wave function, the magnetic system energy, the temperature and the pressure of the target magnetic configuration in a magnetic configuration library; Step 5, return to step 3, select a new random configuration combination in step 2 as the target magnetic configuration and target atomic configuration and repeat steps 4 to 5 until all random configuration combinations have been selected as the target magnetic configuration and target atomic configuration, then execute step 6; Step 6, after the magnetic material is updated, the magneto-optical Kerr measurement system and the X-ray instrument are restarted, and steps 1 to 5 are executed; when the number of magnetic configurations and atomic configurations in the magnetic configuration library reaches a preset value, step 7 is executed; Step 7: Use a superconducting quantum interference device to measure the phase transition temperature of a known magnetic material, and use the phase transition temperature, magnetic configuration, atomic configuration, and magnetic system energy, temperature, and pressure of the known magnetic material to train a deep learning model. Use the trained deep learning model to obtain magnetic materials in a specified phase transition temperature range through a magnetic configuration library.

2. The method for generating a magnetic configuration of a magnetic material according to claim 1, characterized in that: In step 3, the constraint constants and wave functions of the target magnetic configuration are obtained by a self-consistent iteration method, which specifically includes the following steps: Step 331, initialize constraint constant v αa is 0, and the target magnetic moment is obtained according to the target magnetic configuration; Step 332, given the known constraint constant v αa Under the premise of using the Hamiltonian of the initial magnetic configuration to solve the KS equation, the corresponding wave function ψ as the electron density is obtained. The wave function ψ is determined according to the following formula: {H KS +H v }|ψ nσ >=∈ n |ψ nσ >; Among them, H KS is the KS equation, H V is the Hamiltonian of the magnetic system, ψ nσ is the σ component of the spin sub-wave function, ∈ n is the eigenvalue, n is the nth electronic energy level; Step 333, according to the wave function ψ and the constraint constant v αa , using the function M(△v αa ) updates the magnetic moment M of the atom; where, Function M(△v αa ) is determined according to the following formula: {H KS +H v +ΔH v }|ψ nσ 〉=∈ n |ψ nσ > M αa ←∫d 3 rm α (r)Θ(r cut -|r-r a |); Where HV(r) is the Hamiltonian of the magnetic system, vαa is the constraint constant, is the Pauli matrix in the α direction, θ(·) is the cutoff function, r cut is the cutoff radius, r is the real space coordinate, r a is the real space coordinate of atom a, M αa is the magnetic moment of atom in the α direction; m α (r) is the spin density in the α direction, f n is the occupation number of orbital n, ψ nσ is the σ component of the spin sub-wave function, ψ nσ’ is the σ′ component of the spin sub-wave function; d 3 r is the three-dimensional differential symbol; Step 334, determine whether the difference between the magnetic moment of the atom and the target magnetic moment is greater than the set threshold; if it is less than or equal to, execute step 335; if it is greater, update the constraint constant vαa according to the following formula, and return to step 332 to use the new constraint constant: v αa ←v αa +Δv αa ; in is the target magnetic moment; M is the magnetic moment of the atom, vαa is the constraint constant, For M and Optimize the Lagrangian coefficients; Step 335, output the constraint constant vαa and wave function ψ at this time as the constraint constant and wave function of the target magnetic configuration.

3. The method for generating a magnetic configuration of a magnetic material according to claim 1, characterized in that: The specific method for obtaining the energy, temperature and pressure of the magnetic system is: The force on the magnetic moment is obtained based on the constraint constant, and the energy of the magnetic system is obtained based on the wave function and the constraint constant or based on the force on the magnetic moment; the temperature and pressure of the magnetic configuration are obtained based on the force on the magnetic moment, the magnetic moment and the atomic configuration.

4. The method for generating a magnetic configuration of a magnetic material according to claim 1, characterized in that: In step 7, the phase transition temperature, magnetic configuration, atomic configuration, magnetic system energy, temperature and pressure of the known magnetic material are used to train the deep learning model, specifically: The deep learning model is trained by taking the magnetic configuration, atomic configuration, magnetic system energy, temperature and pressure of the known magnetic material as input and the phase transition temperature of the known magnetic material as output.

5. The method for generating a magnetic configuration of a magnetic material according to claim 1, characterized in that: Step 7 also includes: determining the synthesis process of the obtained magnetic material according to the magnetic configuration and phase transition temperature range of the obtained magnetic material.

6. A method for simulating the magnetic configuration of a magnetic material, characterized in that: It includes the following steps: Step 310, obtaining an initial magnetic configuration and an initial atomic configuration; Step 320, setting a target magnetic configuration and a target atomic configuration; obtaining a target magnetic moment according to the target magnetic configuration; Step 330, according to the Hamiltonian of the magnetic configuration, the wave function and the constraint constant are optimized by a self-consistent iteration method, which specifically includes the following steps: Step 3301, initializing the constraint constant vαa to 0, and obtaining the target magnetic moment according to the target magnetic configuration; Step 3302, under the premise of knowing the constraint constant vαa, use the Hamiltonian of the initial magnetic configuration to solve the KS equation to obtain the corresponding wave function ψ as the electron density. Specifically, the wave function ψ is determined according to the following formula: {H KS +H v }|ψ nσ >=∈ n |ψ nσ >; Among them, H KS is the KS equation, H V is the Hamiltonian of the magnetic system, ψn σ is the σ component of the spin sub-wave function, ∈ n is the eigenvalue, n is the nth electronic energy level; Step 3303, according to the wave function ψ and the constraint constant vαa, the magnetic moment M of the atom is updated using the function M(△vαa); wherein, The function M(△vαa) is specifically determined according to the following formula: {H KS +H v +ΔH v }|ψ nσ >=∈ n |ψ nσ > M αa ←∫d 3 rm α (r)Θ(r cut -|r-r a |); Among them, H V (r) is the Hamiltonian of the magnetic system, v αa is the constraint constant, is the Pauli matrix in the α direction, θ(·) is the cutoff function, r cut is the cutoff radius, r is the real space coordinate, r a is the real space coordinate of atom a, M αa is the magnetic moment of atom in the α direction; m α (r) is the spin density in the α direction, f n is the occupation number of orbital n, ψn σ is the σ component of the spin sub-wave function, ψn σ ' is the σ' component of the spin sub-wave function; d 3 r is the three-dimensional differential symbol; Step 3304, determine whether the difference between the magnetic moment of the atom and the target magnetic moment is greater than the set threshold; if it is less than or equal to, execute step 3305; if it is greater, update the constraint constant vαa according to the following formula, and return to step 3302 to use the new constraint constant: v αa ←v αα +Δv αa ; in is the target magnetic moment; v αa is the constraint constant, For M and Optimize the Lagrangian coefficient, where M is the magnetic moment of the atom; Step 3305, outputting the constraint constant vαa and wave function ψ at this time as the constraint constant and wave function of the target magnetic configuration; Step 340, realizing magnetic configuration simulation of magnetic materials: Based on the constraint constants and wave functions, the spatial positions of all constrained atoms corresponding to the magnetic configuration, the final magnetic moment of each atom, the magnetic system energy of the constrained magnetic system, the atomic force exerted on each atom, the magnetic interaction force exerted on each atom and the full-degree-of-freedom constraint field are determined to achieve the simulation of the target magnetic configuration.

7. The method for simulating the magnetic configuration of a magnetic material according to claim 6, characterized in that: The step 330 also includes: Adding a linear energy contribution to the Hamiltonian, wherein the linear energy contribution includes the constraint constant and the difference between the target magnetic moment and the magnetic moment of the atom, and the dimension of the constraint constant is three times the number of atoms, and the constraint constant of any atom in different directions is different; The spatial position of at least one atom in the initial magnetic configuration is fixed, or the magnetic moment of at least one atom in the initial magnetic configuration is fixed, so as to obtain a relaxation process of the magnetic moments of the remaining atoms.

8. A first-principles magnetic confinement method, characterized in that: It includes: Obtaining the magnetic configuration and target magnetic moment of the initial magnetic system; The method comprises obtaining the spatial positions of all atoms corresponding to the magnetic configuration of the initial magnetic system and the magnetic moment of each atom; Based on the first principles, the electron density and constraint constant of the initial magnetic system are optimized according to the system parameters and the target magnetic moment, so as to minimize the energy of the initial magnetic system; including: optimizing the electron density and constraint constant based on self-consistent iteration; in the process of self-consistent iteration, the change introduced in the situation domain is proportional to the constraint constant, and in the process of optimizing the electron density, the constraint constant remains unchanged; in the process of optimizing the constraint constant, the electron density remains unchanged; The magnetic configuration of the constrained magnetic system is determined based on the optimized electron density and the optimized constraint constants. The spatial positions of all constrained atoms, the final magnetic moment of each atom, the system energy of the constrained magnetic system, the atomic force exerted on each atom, the magnetic interaction force exerted on each atom and the full-degree-of-freedom constraint field corresponding to the magnetic configuration of the constrained magnetic system are determined based on the optimized electron density and the optimized constraint constants.

9. The first principle magnetic confinement method according to claim 8, characterized in that: In the process of optimizing the electron density and constraint constant of the initial magnetic system, a linear energy contribution is added to the Hamiltonian. The linear energy contribution includes the constraint constant and the difference between the target magnetic moment and the magnetic moment of the atom. In the linear energy contribution, the dimension of the constraint constant is three times the number of atoms, and the constraint constant of any atom in different directions is different.

10. A magnetic confinement device used in the magnetic confinement method according to the first principle of claim 8, characterized in that: It includes: An acquisition module, used to acquire the magnetic configuration and target magnetic moment of the initial magnetic system; An optimization module, for optimizing the electron density and constraint constant of the initial magnetic system based on the first principles according to the system parameters and the target magnetic moment, so as to minimize the energy of the initial magnetic system; A determination module is used to determine the magnetic configuration of the constrained magnetic system based on the optimized electron density and the optimized constraint constant.

Images