Information processing device, information processing method, and program

Abstract

To enable execution of a variety of computations using a coarse-grained model.SOLUTION: An information processing device is provided, comprising a processor. The processor is configured to use a first model obtained by coarse-graining an all-atom model to generate a second model by reducing the density of the first model, acquire a third model obtained by performing predetermined process on the second model, and acquire a fourth model by compressing the third model obtained through the predetermined processing.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Molecular dynamics simulations are effective in elucidating the relationship between the complex motion of all atoms in materials such as rubber and their material properties. Molecular dynamics simulations often produce arbitrarily created initial structures that are energetically unstable and deviate from models that mimic reality, making it necessary to calculate the equilibrium state through relaxation calculations. While all-atom models that faithfully model polymer structures such as elastomers can converge to an equilibrium state in a short time in single-molecule models in a vacuum, in bulk models where multiple molecules are densely packed, it is difficult to perform calculations until the equilibrium state is reached because the molecules cannot move appropriately due to interference with other molecules.

[0003] In response to this, it is practical to perform relaxation using a coarse-grained model that approximates multiple atoms (e.g., a collection of atoms in a monomer unit) as a single particle. However, there is no established methodology for replacing particles in a coarse-grained model with an all-atom model, and assigning all-atom monomers to coarse-grained particles poses challenges, such as the inability to connect with the all-atom models coordinated to adjacent particles, or the generation of a state in which the distance between all-atom models assigned to other coarse-grained particles, including multi-molecular coarse-grained particles, is too close, resulting in an unstable or impossible molecular dynamics calculation model. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-225226 Summary of the Invention [Problem to be solved by the invention]

[0005] One non-limiting problem that the embodiments of the present disclosure aim to solve is to perform various calculations using a coarse-grained model. The problem that the embodiments of the present disclosure aim to solve is not limited to the above-described problem, and as a further example of some limited problems, it can also be a problem corresponding to the effects described in the embodiments. In other words, a problem that corresponds to at least one of the effects described in the description of the embodiments of the present disclosure can be a problem that the present disclosure aims to solve. [Means for solving the problem]

[0006] According to one embodiment, an information processing device includes a processor that generates a second model by reducing the density of a first model obtained by coarse-graining an all-atom model, performs predetermined processing on the second model to obtain a third model, and compresses the third model to obtain a fourth model. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 10 illustrates an example of coarse-grained model generation according to an embodiment. [Figure 2] 10 is a flowchart illustrating an example of processing according to an embodiment. [Figure 3] 10 is a flowchart illustrating an example of processing according to an embodiment. [Figure 4] FIG. 1 illustrates an example of a coarse-grained model of an equilibrium state according to an embodiment. [Figure 5] FIG. 1 illustrates an example of a coarse-grained model of a low-density equilibrium state according to one embodiment. [Figure 6] FIG. 1 illustrates an example of a low-density equilibrium all-atom model according to one embodiment. [Figure 7] 10 is a flowchart illustrating an example of processing according to an embodiment. [Figure 8] FIG. 1 illustrates an example of a coarse-grained model according to an embodiment. [Figure 9] FIG. 1 illustrates an example of a coarse-grained model according to an embodiment. [Figure 10] FIG. 1 illustrates an example of a coarse-grained model according to an embodiment. [Figure 11] FIG. 1 illustrates an example of a coarse-grained model according to an embodiment. [Figure 12] FIG. 1 illustrates an example of a coarse-grained model according to an embodiment. [Figure 13] FIG. 1 is a diagram showing an example of hardware implementation of an information processing device according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The drawings and the description of the embodiments are provided as examples and are not intended to limit the present invention. In this disclosure, processes are described as being executed by an information processing device, more specifically, a processing circuit within the information processing device, but the present invention is not limited to this. For example, one or more processes may be implemented by multiple information processing devices, or one or more processes may be implemented by multiple various circuits.

[0009] Fig. 1 is a diagram showing an outline of generating a coarse-grained model from an all-atom model according to one embodiment. The all-atom model shown in the figure is a model in which all (almost all) atoms that make up a substance are arranged. Unit particles are assigned to each predetermined atomic structure in this all-atom model, and the structure of these particles is modeled.

[0010] Specifically, by assigning particles to each predetermined atomic structure for the all-atom model, a coarse-grained model consisting of, as a non-limiting example, four particles is generated, as shown in the figure below.

[0011] This predetermined atomic structure may be specified by a user, or an atomic structure that serves as an appropriate unit may be searched for by a processing circuit, etc. As a non-limiting example, the predetermined atomic structure may be a monomer, i.e., a coarse-grained model can be generated by coarse-graining the all-atom model in units of monomers.

[0012] An information processing device according to an embodiment executes processing on a coarse-grained model such as that shown in the lower diagram, thereby realizing the search for states that are difficult to calculate using an all-atom model.

[0013] 2 is a flowchart showing a process according to one embodiment, in which a processing circuit (processor) executes the process according to the flowchart.

[0014] The processing circuit acquires a coarse-grained model obtained by coarse-graining the all-atom model as a first model (S100). The processing circuit can acquire, for example, information about the coarse-grained model. The processing circuit can acquire, for example, information about the coarse-grained model via a network, or can acquire information about the coarse-grained model from a connected storage circuit.

[0015] As another example, the processing circuit can acquire information about the coarse-grained model by acquiring information about the all-atom model and generating a coarse-grained model based on the all-atom model. The processing circuit generates the coarse-grained model (first model), for example, by replacing a predetermined group of atoms in the acquired all-atom model with unit particles. The processing circuit can acquire information about the all-atom model via a network, for example, or can acquire information about the all-atom model from a connected storage circuit.

[0016] The processing circuit calculates an equilibrium state for the acquired (including generated) coarse-grained model (S102). The calculation of the equilibrium state can be performed by any method. The processing circuit may perform the calculation of the equilibrium state based on a classical method, or may perform the calculation of the equilibrium state based on a method using a neural network model such as NNP (Neural Network Potential).

[0017] The processing circuit generates an all-atom model from the equilibrium state of the coarse-grained model, which is the first model (S104). The processing circuit executes processing, which will be described later, on the model representing the equilibrium state of the first model, to obtain a model representing the equilibrium state of the all-atom model, which is the final output result, as the fourth model.

[0018] The processing circuitry outputs the generated all-atom model to an external device or to a storage circuitry within the information processing device, completing the processing (S106). At the same time, the processing circuitry can also output other arbitrary information, such as information on the coarse-grained model used in the processing.

[0019] FIG. 3 is a flowchart specifically showing the process of S104.

[0020] After obtaining a first equilibrium model that is a coarse-grained model of the all-atom model, the processing circuit obtains a second model that is a coarse-grained version of the first equilibrium model (S200).

[0021] 4 is a diagram illustrating an example of a coarse-grained equilibrium model according to an embodiment. For example, in this diagram, five groups of all-molecular models, each consisting of four particles, are arranged as the coarse-grained model. The processing circuitry executes the process of S200 by reducing the density of this coarse-grained equilibrium model.

[0022] 5 is a diagram illustrating an example of a coarse-grained model in a low-density equilibrium state according to one embodiment. The processing circuitry obtains a second model, which is a low-density coarse-grained model, by, for example and not by way of limitation, expanding the space in which the model exists for the coarse-grained model shown in FIG.

[0023] The processing circuitry may generate the second model, for example, by expanding the space by a predetermined factor.

[0024] Furthermore, since it is sufficient that the space be large enough so that the particles of the all-atom model superimposed on the coarse-grained model do not overlap with each other, the processing circuitry may calculate a magnification factor at which the particles of the arranged coarse-grained model do not overlap with each other, and expand the space based on this calculated magnification factor to generate a low-density coarse-grained model.

[0025] 3, the processing circuit places the all-atom model on the second model, which is a low-density coarse-grained model (S202). By performing calculations on this placed all-atom model, the processing circuit roughly constrains the positions of atoms in the all-atom model, making it possible to obtain an equilibrium state more quickly and accurately than when using the all-atom model from the beginning.

[0026] 6 is a diagram illustrating an example of a third model according to an embodiment. The dotted line indicates the second model, which is a coarse-grained model. As shown in this figure, the processing circuitry can generate an all-atom model based on the coarse-grained model in a low-density equilibrium state by superimposing the all-atom model on the coarse-grained model.

[0027] The processing circuit generates a third model for the second model by, for example, superimposing all-atom models for each particle (e.g., monomer) set in the process of Fig. 1. The processing circuit superimposes all atoms for all particles.

[0028] 2, when information on the coarse-grained model is acquired directly from the outside, the information processing device can arrange all atoms by acquiring information on the type of atomic group each particle in the coarse-grained model constitutes. When coarse-graining is performed in the processing circuit, the processing circuit generates an all-atom model based on the information obtained at the time of coarse-graining.

[0029] 3, the processing circuitry executes a predetermined process on the all-atom model acquired in S202 to determine the coordinates of atoms in the all-atom model (S204). The predetermined process will be described later.

[0030] The processing circuit acquires a fourth model, which is a high-density all-atom model, from the all-atom model whose coordinates have been determined (S206). The processing circuit generates a high-density all-atom model by performing the reverse operation of S200. The processing circuit can generate the fourth model, for example, by compressing the space in which the third model exists by the magnification ratio used for expansion in S200.

[0031] 7 is a flowchart showing the processing of S204 according to one embodiment. For example, the processing circuitry performs optimization using the all-atom model acquired in S202 as an initial value to search for an arrangement of atoms in a more equilibrium state for the all-atom model.

[0032] The processing circuit updates the coordinates of the atoms in the third model to a stable state (S300). This coordinate updating may be performed using a classical method or a method such as NNP. Furthermore, instead of using physical properties such as energy, the processing circuit may also perform sequential optimization of the atomic arrangement based on the relationship between the particles represented in the second model and the atoms included in the all-atom model.

[0033] The processing circuit determines whether the all-atom model at the coordinates updated in S300 satisfies a predetermined condition (S302). If the predetermined condition is satisfied (S302: YES), the all-atom model indicated by the coordinates acquired in S300 is set as the final third model, and in S206, the final third model is compressed to generate a fourth model.

[0034] If the predetermined condition is not satisfied (S302: NO), the processing circuit repeats updating the coordinates of the atoms in the third model until the predetermined condition is satisfied.

[0035] The specified conditions may be set depending on the optimization method used, and can be expressed, for example, as conditions such as a specified number of coordinate updates being performed, a specified evaluation value becoming smaller (larger) than a threshold value, or a specified period of coordinate updates being performed.

[0036] Next, each process will be described using a more specific example.

[0037] (Example 1)

[0038] In the process of S102, the processing circuit may repeatedly perform equilibrium calculations for the particles in the first model until the energy becomes stable to a certain extent. As described above, the processing circuit can perform calculations to determine this equilibrium state using a classical method or a method such as NNP.

[0039] (Example 2)

[0040] In at least one of the processes of S200 and S206, the processing circuitry may use a pressure change process instead of changing the space (cell) size as the density conversion process described above. For example, the processing circuitry may generate a second model by lowering the pressure in the first model. Conversely, the processing circuitry may generate a fourth model by increasing the pressure in the updated third model.

[0041] The processes of S200 and S206 may be the same process, for example, if the space is expanded in S200, the space may be reduced in S206, but this is not limited to this. For example, the processing circuit may expand the space in S200 and increase the pressure in S206.

[0042] (Example 3)

[0043] Furthermore, in at least one of the processes of S200 and S206, the processing circuitry can change the density so that the conformation of the coarse-grained model does not change.

[0044] (Example 4)

[0045] Figure 8 shows an example of the arrangement of a coarse-grained model. When periodic boundary conditions are used, particles that form a polymer may be arranged across the cells where calculations are performed. In this case, the processing circuit can perform calculations using coordinates where the periodic boundary conditions are canceled, rather than the coordinates of the cells set under the periodic boundary conditions.

[0046] FIG. 9 is a diagram showing an example in which the periodic boundary conditions have been released. The particles represented by dotted lines are particles whose arrangement has been changed to ensure appropriate connections based on the periodic boundary conditions. As shown in FIG. 9, the particles represented by dotted lines can be rearranged to ensure appropriate particle connections. The processing circuitry can perform various calculations, such as calculating the equilibrium state, based on this coarse-grained model in which the periodic boundary conditions have been released.

[0047] Furthermore, the processing circuitry can also execute calculations by changing the absolute coordinates (moving some particles) so that the relative positions under the periodic boundary conditions do not change.

[0048] Figure 10 shows a model in which some particles have been moved to other cells. The processing circuit can also use this model in which some particles have been moved to other cells to perform calculations on a model set under periodic boundary conditions.

[0049] 8 to 10 show coarse-grained models, but the present invention is not limited to these. The processing circuit can also perform various calculations on all-atom models after performing appropriate processing based on periodic boundary conditions.

[0050] (Example 5)

[0051] In the process of S202, the processing circuitry may first arrange a representative group of atoms from among the groups of atoms constituting the all-atom model so as to be superimposed on the coarse-grained model, rather than simultaneously arranging all atoms of the all-atom model on the coarse-grained model, and then superimpose the other atoms of the all-atom model on the coarse-grained model based on the representative group of atoms.

[0052] Representative atomic groups can be, for example, superimposed by specifying atoms present at the edges of particles that form a polymer, and based on these atoms, other atoms can be arranged to form an appropriate polymer path.

[0053] For example, the processing circuit can omit smaller atoms such as hydrogen and designate atoms such as carbon as the main group, or atoms such as sulfur in the case of crosslinking, as the representative atom of the atomic group. For example, in the case of a terminally modified polymer, the processing circuit can designate the terminally modified group as the representative atom of the atomic group.

[0054] The processing circuit can arrange atoms so as to form a polymer path in the all-atom model that is close to the polymer path in the coarse-grained model by arranging atoms of the representative atomic group and then arranging other atoms. By performing this processing, the information processing device can reduce the amount of calculation and improve calculation efficiency.

[0055] The above calculation can be similarly executed not only in S202 but also in any of the processes in S202 to S204.

[0056] (Example 6)

[0057] The processing circuit can perform optimization by sequentially processing the third model. For example, as described above, by setting appropriate conditions as the judgment conditions in S302, it is possible to sequentially arrange atoms in the all-atom model.

[0058] The optimization of the atomic arrangement can be performed using simulations such as molecular dynamics simulation and Monte Carlo method, as well as optimization methods such as gradient methods. When performing a search using errors in atomic coordinates, the processing circuitry can use, as errors, errors such as an error caused by a predetermined number or more atoms of the all-atom model falling within a particle of the coarse-grained model, an error caused by a predetermined number or more atoms of the all-atom model falling within a tube radius of the coarse-grained model, but is not limited to these.

[0059] By comparing the error with a threshold value, the processing circuit can terminate the processing when the error falls within an allowable range, thereby enabling efficient model creation.

[0060] (Example 7)

[0061] FIG. 11 illustrates an example of a coarse-grained model according to an embodiment. The processing circuitry can place virtual atoms, which are imaginary particles indicated by diagonal lines, within the particles forming the coarse-grained model. The processing circuitry can place atoms of the all-atom model based on the virtual atoms and update the coordinates of the atoms. The processing circuitry can also place virtual atoms at the center of each particle of the coarse-grained model.

[0062] Fig. 12 is a diagram showing another embodiment of Fig. 11. The processing circuitry may set a curve (shown by a thick line) to connect the centers of the particles and place virtual atoms on this curve. The processing circuitry may set this curve, for example, by a spline curve connecting the centers of the particles. After setting the curve, the processing circuitry can place virtual atoms on the curve.

[0063] In placing atoms in S202 or updating coordinates in S302, the processing circuitry can perform the placement or update of atoms based on this virtual atom.

[0064] The processing circuit may, for example, arrange or update the position of the virtual atom so that the atom included in the all-atom model overlaps with the position of the virtual atom.

[0065] When updating the coordinates of the all-atom model in molecular dynamics or the like, the processing circuitry can set attractive interactions between the virtual atoms and atoms of the all-atom model and update the coordinates of the atoms included in the all-atom model.

[0066] When a gradient-based optimization method such as steepest descent or conjugate gradient method is used, the processing circuit may set new coordinates by updating the atoms by shifting them in the normal direction of the set curve.

[0067] The processing circuitry may update the atoms of all molecules in the all-atom model simultaneously, or may update each molecule individually. Furthermore, the processing circuitry may update the atomic coordinates included in each partial region.

[0068] By setting virtual atoms in this way and setting and updating the atomic arrangement, it is possible to efficiently determine the arrangement of the all-atom model while taking into account the interaction of the atoms of the all-atom model with the particles of the coarse-grained model.

[0069] (Example 8)

[0070] When updating the atomic coordinates of the all-atom model, the processing circuitry may not update pre-specified atomic coordinates or may change them by a smaller amount than for other atoms. For example, the initial coordinates of polymer ends, crosslinking points, entanglement points, etc. may be set to match the coarse-grained model, and by not updating these initial coordinates or by changing them by a smaller amount, a search can be performed while maintaining the general structure.

[0071] In addition, the processing circuit can reduce the amount of change by increasing the mass of atoms compared to other atoms in methods such as molecular dynamics, or by setting a penalty for change in methods such as gradient methods.

[0072] By partially restraining the coordinates, it is possible to efficiently converge by searching without significantly moving the coordinates of the predetermined atoms that are approximated in the coarse-grained model.

[0073] Note that partial constraints are not limited to constraining the positions of specific atoms, but can also be other constraints such as the coincidence of the center of gravity in a partial region with the center of gravity of a particle in the coarse-grained model, or the rotation or deformation of a monomer in the all-atom model at the connection point of a particle in the coarse-grained model.

[0074] (Example 9)

[0075] The processing circuit may compress the all-atom model while updating the coordinates of the atoms in the process of S206. For example, the processing circuit may move the coordinates of the atoms while adjusting the portions where atoms of the all-atom model protrude from the particles of the coarse-grained model or overlap with adjacent monomers.

[0076] When performing this process, the processing circuit may further perform a process to prevent the distance between other atoms from becoming too small. That is, when the distance between two atoms becomes too small, the processing circuit can perform the compression process while increasing the distance between the atoms by making them repel each other in consideration of the repulsive force between the atoms.

[0077] In this process of generating a high-density all-atom model, the coordinates of the atoms may be changed appropriately, which can further improve the stability of the resulting high-density all-atom model.

[0078] (Example 10)

[0079] In each of the above examples, when using physical property values ​​such as energy, it is possible to use NNP as appropriate. That is, the processing circuit can use NNP for at least some of the calculations in the above series of flows. By using NNP, it is possible to achieve calculations with higher accuracy.

[0080] (Example 11)

[0081] In the above description, the processing circuit performs an equilibrium calculation to obtain an equilibrium state, but this is not limited to this. The processing circuit can obtain an equilibrium state by performing an operation including a process of deforming the system, in addition to an equilibrium calculation that reduces and stabilizes the energy by evolving the system over time without deforming it.

[0082] The processing circuitry can perform a transformation such as stretching or shearing a system (including the concept of a cell), and then perform a calculation to obtain an equilibrium state after performing this transformation process. The processing circuitry can also perform a calculation to isotropically expand or compress a system (including the concept of a cell), and obtain an equilibrium state for the expanded or compressed system.

[0083] As described above, according to some embodiments of the present disclosure, an information processing device can efficiently and accurately calculate the equilibrium state of a coarse-grained model. High-speed calculations can be achieved by, for example, expanding cells in the coarse-grained model while maintaining the conformation of each molecule. Furthermore, by arranging atoms in the all-atom model so that the polymer path of the all-atom model substantially matches the polymer path of the coarse-grained model, a search for an all-atom model that more closely matches the equilibrium state calculated using the coarse-grained model can be achieved. As a result, a search for an equilibrium state can be implemented quickly and accurately.

[0084] Some or all of the devices (information processing devices) in the above-described embodiments may be configured as hardware, or may be configured as software (programs) executing information processing by a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), etc. When software information processing is configured, software that realizes at least some of the functions of each device in the above-described embodiments may be stored on a non-transitory storage medium (non-transitory computer-readable medium) such as a CD-ROM (Compact Disc-Read Only Memory) or a USB (Universal Serial Bus) memory, and the software information processing may be executed by loading the software into a computer. The software may also be downloaded via a communications network. Furthermore, all or part of the software processing may be implemented in a circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array), thereby allowing the software information processing to be executed by hardware.

[0085] The storage medium that stores the software may be a removable medium such as an optical disk, or a fixed medium such as a hard disk or memory. The storage medium may be located inside the computer (such as a main memory or auxiliary memory), or may be located outside the computer.

[0086] 13 is a block diagram showing an example of the hardware configuration of each device (information processing device) in the above-described embodiment. Each device may be realized as a computer 7 including, for example, a processor 71, a main storage device 72 (memory), an auxiliary storage device 73 (memory), a network interface 74, and a device interface 75, all of which are connected via a bus 76.

[0087] Although the computer 7 in FIG. 13 includes one of each component, it may include multiple of the same component. Also, while FIG. 13 shows one computer 7, the software may be installed on multiple computers, and each of the multiple computers may execute the same or different parts of the software. In this case, a distributed computing configuration may be used in which each computer communicates with the other computers via a network interface 74 or the like to execute the processing. In other words, each device (information processing device) in the above-described embodiment may be configured as a system in which one or more computers execute instructions stored in one or more storage devices to achieve its function. Furthermore, the system may be configured such that information sent from a terminal is processed by one or more computers located on a cloud, and the processing results are sent to the terminal.

[0088] The various calculations of each device (information processing device) in the above-described embodiments may be executed in parallel using one or more processors, or using multiple computers connected via a network. Furthermore, the various calculations may be distributed to multiple processor cores within a processor and executed in parallel. Furthermore, some or all of the processes, means, etc. disclosed herein may be implemented by at least one processor and storage device provided on a cloud that can communicate with computer 7 via a network. Thus, each device in the above-described embodiments may be implemented in the form of parallel computing using one or more computers.

[0089] The processor 71 may be an electronic circuit (CPU, GPU, FPGA, ASIC, etc.) that performs at least one of computer control and calculation. The processor 71 may also be a general-purpose processor, a dedicated processing circuit designed to perform a specific calculation, or a semiconductor device that includes both a general-purpose processor and a dedicated processing circuit. The processor 71 may also include an optical circuit or a calculation function based on quantum computing.

[0090] The processor 71 may perform arithmetic processing based on data or software input from each device, etc., configured inside the computer 7, and may output the calculation results or control signals to each device, etc. The processor 71 may control each component constituting the computer 7 by executing the OS (Operating System) of the computer 7, applications, etc.

[0091] Each device (information processing device) in the above-described embodiments may be realized by one or more processors 71. Here, the processor 71 may refer to one or more electronic circuits arranged on one chip, or may refer to one or more electronic circuits arranged on two or more chips or two or more devices. When multiple electronic circuits are used, the electronic circuits may communicate with each other via wire or wirelessly.

[0092] The main memory device 72 may store instructions executed by the processor 71 and various data, etc., and information stored in the main memory device 72 may be read by the processor 71. The auxiliary memory device 73 is a memory device other than the main memory device 72. Note that these memory devices refer to any electronic component capable of storing electronic information and may be semiconductor memory. The semiconductor memory may be either volatile or nonvolatile memory. The memory device for saving various data, etc. in each device (information processing device) in the above-described embodiments may be realized by the main memory device 72 or the auxiliary memory device 73, or may be realized by an internal memory built into the processor 71. For example, the memory unit in the above-described embodiment may be realized by the main memory device 72 or the auxiliary memory device 73. For example, at least some of the operations in the present disclosure may be implemented by the processor constructing a trained model by referring to data related to the trained model stored in a memory circuit. The memory device stores, for example, data related to a trained model that outputs physical property values ​​when molecular information is input. For example, the processor uses the trained model to perform a simulation in which multiple molecular models are adsorbed onto multiple adsorption sites. The trained model is, for example, a model used in NNP (Neural Network Potential). For example, the physical property values ​​include at least the energy or force of the molecules.

[0093] When each device (information processing device) in the above-described embodiments is configured with at least one storage device (memory) and at least one processor connected (coupled) to this at least one storage device, at least one processor may be connected to one storage device. At least one storage device may be connected to one processor. A configuration in which at least one processor among multiple processors is connected to at least one storage device among multiple storage devices may also be included. This configuration may also be realized by storage devices and processors included in multiple computers. Furthermore, a configuration in which a storage device is integrated with a processor (for example, a cache memory including an L1 cache and an L2 cache) may also be included.

[0094] The network interface 74 is an interface for connecting to the communication network 8 wirelessly or via a wire. The network interface 74 may be an appropriate interface, such as one that conforms to an existing communication standard. The network interface 74 may exchange information with an external device 9A connected via the communication network 8. The communication network 8 may be any one of a WAN (Wide Area Network), a LAN (Local Area Network), a PAN (Personal Area Network), etc., or a combination thereof, as long as information is exchanged between the computer 7 and the external device 9A. An example of a WAN is the Internet, an example of a LAN is IEEE 802.11 or Ethernet (registered trademark), and an example of a PAN is Bluetooth (registered trademark) or NFC (Near Field Communication), etc.

[0095] The device interface 75 is an interface such as USB that directly connects to the external device 9B.

[0096] The external device 9A is a device connected to the computer 7 via a network. The external device 9B is a device directly connected to the computer 7.

[0097] For example, the external device 9A or the external device 9B may be an input device. The input device may be a device such as a camera, a microphone, a motion capture device, various sensors, a keyboard, a mouse, or a touch panel, and provides acquired information to the computer 7. Alternatively, the external device 9A or the external device 9B may be a device equipped with an input unit, a memory, and a processor, such as a personal computer, a tablet terminal, or a smartphone.

[0098] Furthermore, the external device 9A or the external device 9B may be, for example, an output device. The output device may be, for example, a display device such as an LCD (Liquid Crystal Display) or an organic EL (Electro Luminescence) panel, or a speaker that outputs sound or the like. Alternatively, the external device 9A or the external device 9B may be a device including an output unit, a memory, and a processor, such as a personal computer, a tablet terminal, or a smartphone.

[0099] Furthermore, the external device 9A or the external device 9B may be a storage device (memory). For example, the external device 9A may be a network storage or the like, and the external device 9B may be a storage such as an HDD.

[0100] Furthermore, the external device 9A or the external device 9B may be a device having some of the functions of the components of each device (information processing device) in the above-described embodiment. That is, the computer 7 may transmit some or all of the processing results to the external device 9A or the external device 9B, or may receive some or all of the processing results from the external device 9A or the external device 9B.

[0101] Although the embodiments of the present disclosure have been described in detail above, the present disclosure is not limited to the individual embodiments described above. Various additions, modifications, substitutions, and partial deletions are possible within the scope of the conceptual idea and spirit of the present disclosure, which is derived from the content defined in the claims and their equivalents. For example, when numerical values ​​or formulas are used in the above-described embodiments, they are shown for illustrative purposes and do not limit the scope of the present disclosure. Furthermore, the order of each operation shown in the embodiments is also illustrative and does not limit the scope of the present disclosure.

[0102] The above embodiment can be summarized as follows.

[0103] (1) a processor; The processor: A second model is generated by reducing the density of a first model obtained by coarse-graining an all-atom model; obtaining a third model by performing a predetermined process based on the second model; compressing the third model that has undergone the predetermined processing to obtain a fourth model; Information processing device.

[0104] (2) The processor: generating the first model from the all-atom model using a predetermined atomic group as a unit; An information processing device according to (1).

[0105] (3) The processor: performing a predetermined operation on the first model; obtaining an equilibrium state of the first model based on a result of the predetermined calculation; generating the second model from the first model that has acquired the equilibrium state; (2) An information processing device according to the present invention.

[0106] (4) The processor: superimposing an all-atom model on the second model to obtain the third model; An information processing device according to (2) or (3).

[0107] (5) The processor: generating the third model by arranging a representative group of atoms among the groups of atoms constituting the all-atom model based on the second model; (4) An information processing device according to the present invention.

[0108] (6) The processor: After arranging the representative group of atoms, other atoms of the group of atoms constituting the all-atom model are arranged to generate the third model. (5) An information processing device according to the present invention.

[0109] (7) The processor: Optimizing and updating the coordinates of atoms included in the all-atom model for the third model until the coordinates satisfy a predetermined condition. An information processing device according to any one of (4) to (6).

[0110] (8) The processor: a virtual atom is arranged for the unit in the second model, and the coordinates of the all-atom model are updated so that the virtual atom coincides with the atoms included in the all-atom model, thereby updating the third model; (7) An information processing device according to (7).

[0111] (9) The processor: updating the third model without updating the atomic coordinates of a predetermined atom included in the all-atom model, or by updating the atomic coordinates of the predetermined atom with a smaller change amount in the update than that of other atoms; An information processing device according to any one of (4) to (8).

[0112] (10) The processor: compressing the third model while updating the coordinates to obtain the fourth model; An information processing device according to any one of (4) to (8).

[0113] (11) The processor: For a plurality of atoms whose interatomic distance is shorter than a predetermined distance, compressing the plurality of atoms to repel each other and increase the distance to obtain the fourth model. (10) An information processing device according to (10).

[0114] (12) The all-atom model is a model including an interface with an inorganic material. An information processing device according to any one of (1) to (11).

[0115] (13) The processor: At least some of the processing uses Neural Network Potential (NNP) techniques. An information processing device according to any one of (1) to (12).

[0116] (14) The processor: A second model is generated by reducing the density of a first model obtained by coarse-graining an all-atom model; obtaining a third model by performing a predetermined process based on the second model; compressing the third model that has undergone the predetermined processing to obtain a fourth model; Information processing methods.

[0117] (15) The processor A second model is generated by reducing the density of a first model obtained by coarse-graining an all-atom model; obtaining a third model by performing a predetermined process based on the second model; compressing the third model that has undergone the predetermined processing to obtain a fourth model; A program that executes a process. [Explanation of symbols]

[0118] 1: Information processing system, 10: Information processing device, 100: Input / output I / F, 102: Memory section, 104: Processing circuit, 20: Storage, 30: Learning device, 40: Estimation device, 50: Exploration device, 7: Computer, 71: Processor, 72: Main storage, 73: Auxiliary storage, 74: Network interface, 75: Device Interface, 76: Bus, 8: Communication networks, 9A, 9B: External device

Claims

1. a processor; The processor: A second model is generated by reducing the density of a first model obtained by coarse-graining an all-atom model; obtaining a third model by performing a predetermined process based on the second model; compressing the third model that has undergone the predetermined processing to obtain a fourth model; Information processing device.

2. The processor: generating the first model from the all-atom model using a predetermined atomic group as a unit; The information processing device according to claim 1.

3. The processor: performing a predetermined operation on the first model; obtaining an equilibrium state of the first model based on a result of the predetermined calculation; generating the second model from the first model that has acquired the equilibrium state; The information processing device according to claim 2.

4. The processor: superimposing an all-atom model on the second model to obtain the third model; The information processing device according to claim 2.

5. The processor: generating the third model by arranging a representative group of atoms among the groups of atoms constituting the all-atom model based on the second model; The information processing device according to claim 4.

6. The processor: After arranging the representative group of atoms, other atoms of the group of atoms constituting the all-atom model are arranged to generate the third model. The information processing device according to claim 5.

7. The processor: Optimizing and updating the coordinates of atoms included in the all-atom model for the third model until the coordinates satisfy a predetermined condition. The information processing device according to claim 4.

8. The processor: a virtual atom is arranged for the unit in the second model, and the coordinates of the all-atom model are updated so that the virtual atom coincides with the atoms included in the all-atom model, thereby updating the third model; The information processing device according to claim 7.

9. The processor: updating the third model without updating the atomic coordinates of a predetermined atom included in the all-atom model, or by updating the atomic coordinates of the predetermined atom with a smaller change amount in the update than that of other atoms; The information processing device according to claim 4.

10. The processor: compressing the third model while updating the coordinates to obtain the fourth model; The information processing device according to claim 4.

11. The processor: For a plurality of atoms whose interatomic distance is shorter than a predetermined distance, compressing the plurality of atoms to repel each other and increase the distance to obtain the fourth model. The information processing device according to claim 10.

12. The all-atom model is a model including an interface with an inorganic material. The information processing device according to claim 1.

13. The processor: At least some of the processing uses Neural Network Potential (NNP) techniques. The information processing device according to claim 1.

14. The processor: A second model is generated by reducing the density of a first model obtained by coarse-graining an all-atom model; obtaining a third model by performing a predetermined process based on the second model; compressing the third model that has undergone the predetermined processing to obtain a fourth model; Information processing methods.

15. The processor A second model is generated by reducing the density of a first model obtained by coarse-graining an all-atom model; obtaining a third model by performing a predetermined process based on the second model; compressing the third model that has undergone the predetermined processing to obtain a fourth model; A program that executes a process.

Images