Prediction method, computer program, prediction device, and method for manufacturing metal-organic structure

Abstract

Provided are, inter alia, a prediction method with which it is possible to predict the adsorption characteristics of a metal-organic structure.　In this prediction method, a computer executes a process for: acquiring an initial structure of a metal-organic structure; deriving, by performing a simulation using a Monte Carlo method, the disposition of a body subject to adsorption when the body subject to adsorption is adsorbed to the metal-organic structure of the initial structure; and acquiring a prediction value of the adsorption characteristics of the metal-organic structure having a structure corresponding to the derived disposition.

Description

Prediction method, computer program, prediction device, and metal-organic framework manufacturing method

[0001] The present invention relates to a prediction method, a computer program, a prediction device, and a method for producing a metal-organic framework.

[0002] Conventionally, the physical properties of materials have been predicted by computer simulations. For example, Patent Literature 1 discloses a physical property prediction method for easily predicting the interfacial energy of a metallic material by simulation based on molecular dynamics.

[0003] Japanese Patent Application Laid-Open No. 2022-62524

[0004] The technique described in Patent Document 1 does not predict the adsorption properties of a metal-organic framework.

[0005] An object of the present disclosure is to provide a prediction method and the like that can predict the adsorption properties of a metal-organic framework.

[0006] In a prediction method according to one aspect of the present disclosure, a computer executes a process of acquiring an initial structure of a metal-organic framework, determining the arrangement of an adsorbate when the adsorbate is adsorbed onto the metal-organic framework having the initial structure through a simulation using a Monte Carlo method, and acquiring a predicted value of the adsorption characteristics of the metal-organic framework having a structure corresponding to the determined arrangement.

[0007] A computer program according to one aspect of the present disclosure causes a computer to execute a process of acquiring an initial structure of a metal-organic framework, determining a configuration of an adsorbate when the adsorbate is adsorbed onto the metal-organic framework having the initial structure by a simulation using a Monte Carlo method, and acquiring a predicted value of the adsorption characteristics of the metal-organic framework having a structure corresponding to the determined configuration.

[0008] A prediction device according to one aspect of the present disclosure includes a control unit that executes a process of acquiring an initial structure of a metal-organic framework, determining a configuration of an adsorbate when the adsorbate is adsorbed onto the metal-organic framework having the initial structure through a simulation using a Monte Carlo method, and acquiring a predicted value of the adsorption characteristics of the metal-organic framework having a structure corresponding to the determined configuration.

[0009] A method for producing a metal-organic framework according to one aspect of the present disclosure includes the steps of: acquiring initial structures of a plurality of metal-organic frameworks; determining, for each metal-organic framework, a configuration of an adsorbate when the adsorbate is adsorbed onto the metal-organic framework having the initial structure by simulation using a Monte Carlo method; acquiring predicted values ​​of adsorption properties of a metal-organic framework having a structure corresponding to the determined configuration; selecting a metal-organic framework for which the acquired predicted value of adsorption properties satisfies a predetermined condition; and obtaining the selected metal-organic framework.

[0010] According to the present disclosure, the adsorption properties of a metal-organic framework can be predicted.

[0011] Fig. 1 is a block diagram showing an example of the configuration of a manufacturing system; Fig. 2 is a flowchart showing an example of a prediction processing procedure executed by a prediction device; Fig. 3 is a flowchart showing an example of a selection processing procedure executed by a prediction device; Fig. 4 is a diagram showing predicted values ​​and experimental values ​​of an adsorption isotherm; Fig. 5 is a diagram showing predicted values ​​and experimental values ​​of an adsorption isotherm.

[0012] The present disclosure will be specifically described with reference to the drawings showing embodiments thereof.

[0013] (First Embodiment) FIG. 1 is a block diagram showing an example of the configuration of a manufacturing system 100. The manufacturing system 100 includes a prediction device 1 and a manufacturing device 2. For the purpose of research and development of new substances and alternative substances, the manufacturing system 100 predicts the physical properties of multiple candidate substances to search for substances that can be manufactured, and manufactures substances according to the search results. The manufacturing system 100 of this embodiment predicts the adsorption properties of the porous material, thereby manufacturing a porous material that satisfies desired adsorption properties. In this specification, "adsorption properties" refer to the property of a metal-organic framework to adsorb an adsorbate. The "adsorbate" may be an atom or a molecule.

[0014] In the following, we will predict the water adsorption properties of a metal organic framework (hereinafter also referred to as MOF). MOFs are materials also known as porous coordination polymers (PCPs), and have a high surface area coordination network structure formed by interactions (e.g., coordinate bonds) between metals and organic ligands. MOFs are used as a variety of materials because they exhibit the ability to adsorb or desorb adsorbates such as water, gases, and organic molecules due to the structure described above.

[0015] The molecule for which the adsorption characteristics are predicted is not limited to water, and may be, for example, a gas, an organic molecule, etc. Examples of gases include carbon dioxide, hydrogen, carbon monoxide, oxygen, nitrogen, hydrocarbons having 1 to 4 carbon atoms, rare gases, hydrogen sulfide, ammonia, sulfur oxides, nitrogen oxides, and siloxanes. Examples of organic molecules include hydrocarbons having 5 to 8 carbon atoms, alcohols having 1 to 8 carbon atoms, aldehydes having 1 to 8 carbon atoms, carboxylic acids having 1 to 8 carbon atoms, ketones having 1 to 8 carbon atoms, amines having 1 to 8 carbon atoms, esters having 1 to 8 carbon atoms, and amides having 1 to 8 carbon atoms. The organic molecules may contain an aromatic ring.

[0016] The porous material whose physical properties are to be predicted is not limited to MOF, but may also be, for example, zeolite, porous silica, porous polymer, etc.

[0017] The prediction device 1 is an information processing device capable of various information processing and information transmission / reception, such as a personal computer, server computer, or quantum computer. The prediction device 1 predicts the water adsorption properties of multiple candidate MOFs. Based on the prediction results, MOFs that can satisfy the desired water adsorption properties can be selected from the candidate substances.

[0018] The production apparatus 2 produces the selected MOF. The production apparatus 2 includes, for example, a mixing section (not shown) for mixing the raw materials for the MOF, and produces the MOF by mixing the raw materials. The production apparatus 2 may be configured appropriately depending on the substance to be produced.

[0019] 1, the prediction device 1 includes a control unit 11, a storage unit 12, a communication unit 13, an input unit 14, and an output unit 15. The prediction device 1 may be configured to perform distributed processing using multiple computers, may be realized by multiple virtual machines provided in a single server, or may be realized using a cloud server.

[0020] The control unit 11 includes a processor using one or more central processing units (CPUs), graphics processing units (GPUs), etc. The control unit 11 controls each component and executes processing using built-in memories such as read-only memory (ROM) or random access memory (RAM), clocks, counters, etc. The functional units of the control unit 11 may be implemented by software, or some or all of them may be implemented by hardware such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0021] The storage unit 12 includes a non-volatile memory such as a hard disk, a flash memory, or an SSD (Solid State Drive). The storage unit 12 may be an external storage device connected to the prediction device 1. The storage unit 12 stores various computer programs and data referenced by the control unit 11. The storage unit 12 of this embodiment stores a program 1P for causing a computer to execute processing related to the prediction of physical properties.

[0022] A computer program (program product) including program 1P may be provided by a non-transitory recording medium 1A on which the computer program is readably recorded. The storage unit 12 stores the computer program read from the recording medium 1A by a reading device (not shown). The recording medium 1A may be, for example, a magnetic disk, an optical disk, or a semiconductor memory. Alternatively, the computer program may be downloaded from an external server connected to a communications network and stored in the storage unit 12. Program 1P may be a single computer program or may be composed of multiple computer programs, and may be executed on a single computer or on multiple computers interconnected by a communications network.

[0023] The communication unit 13 includes a communication module for communicating with an external device via a network (not shown). The control unit 11 transmits and receives data to and from the external device via the communication unit 13. The communication unit 13 may be omitted.

[0024] The input unit 14 accepts input of various data necessary for carrying out property prediction, such as initial information used for property prediction, calculation conditions, required properties, etc. The input unit 14 sends the accepted input contents to the control unit 11. The input unit 14 includes, for example, a keyboard, a mouse, a touch panel device with a built-in display, an interface for importing data from the outside, etc.

[0025] The output unit 15 outputs various data associated with the performance of property prediction, such as predicted properties, selection results of candidate substances, etc. The output unit 15 outputs various information in accordance with instructions from the control unit 11. The output unit 15 includes, for example, a display device.

[0026] The prediction device 1 may be configured to receive an operation via an externally connected computer and output information to be notified to the external computer. In this case, the prediction device 1 does not need to include the input unit 14 and the output unit 15.

[0027] The prediction method of this embodiment includes a simulation step using the Monte Carlo (MC) method and a simulation step using the molecular dynamics (MD) method, and these two steps are repeatedly executed. By combining the two types of simulation steps, the accuracy of physical property prediction is improved.

[0028] 2 is a flowchart showing an example of a prediction process procedure executed by the prediction device 1. The processes in the following flowcharts are executed by the control unit 11 in accordance with a program 1P stored in the storage unit 12 of the prediction device 1.

[0029] The control unit 11 of the prediction device 1 acquires an initial structure corresponding to the composition of the MOF to be simulated (step S11). The initial structure is provisional crystal structure data determined according to the composition and includes information indicating the position of each atom. The control unit 11 determines the initial structure, for example, by generating candidate MOFs on a computer and assigning provisional coordinates to each atom constituting the generated MOF. The initial structure acquired in step S11 may be a structure optimized using an optimization method such as the BFGS method.

[0030] The control unit 11 acquires the initial state for the MOF to be simulated (step S12). The initial state is set in advance according to the MOF to be simulated. The initial state includes, for example, the water pressure (partial pressure) and the system temperature. The partial pressure may be expressed as the relative pressure P / P0 of water vapor (the adsorbate in a gaseous state). Here, P indicates the equilibrium pressure of water vapor, and P0 indicates the saturated vapor pressure. Multiple partial pressures P / P0 may be set, and may be one or more values ​​between 0.05 and 0.5, for example. The initial state is used as a calculation condition in a series of simulation steps.

[0031] The control unit 11 acquires a force field (potential) to be applied to the simulation (step S13). In this embodiment, a neural network potential (hereinafter also referred to as NNP) is used as the force field. The neural network potential is a type of machine learning potential in which atomic interactions are learned using a neural network, and is highly versatile. The neural network potential can be defined using a known method.

[0032] The control unit 11 executes a simulation using the Monte Carlo method based on the acquired initial structure, initial state, and force field (step S14) to calculate the optimal particle arrangement. Specifically, the control unit 11 generates a model in which particles are arranged in a cell used in the simulation based on the acquired initial structure and initial state. The cell is defined as, for example, a rectangular parallelepiped or a cube. The size of the cell can be set appropriately depending on the total number of MOFs and water molecules to be arranged in the cell. The control unit 11 evaluates the generated model using the Monte Carlo method to calculate the optimal particle arrangement when water is adsorbed or desorbed, and determines the structure after adsorption or desorption. The optimal arrangement may be the most stable and most feasible arrangement.

[0033] The Monte Carlo method is a technique for numerically calculating partition functions in configuration space in statistical mechanics. A preferred Monte Carlo method is the Grand Canonical Ensemble Monte Carlo method (hereinafter also referred to as the GCMC method). In the GCMC method, the temperature of the system is controlled to be constant throughout the entire simulation, and the system allows the exchange of energy and particles (specifically, atoms and molecules).

[0034] The GCMC method calculates the phase equilibrium state of a cell, which is a mixed system of MOFs and particles. In the GCMC method, the volume of the cell is kept constant, and various thermodynamic equilibrium states are reproduced by repeating the stochastic movement (translation, rotation), insertion, and deletion of particles.

[0035] The control unit 11 performs Monte Carlo calculations according to predetermined calculation conditions. The calculation conditions may be manually set in advance and stored in the storage unit 12. The calculation conditions may include the number of MC steps, boundary conditions, interaction cutoff distances, trial probabilities, etc. The trial probabilities include the probabilities of move operations, insert operations, and delete operations. The adoption of each operation may be determined by the Metropolis method. The calculation conditions may be appropriately selected according to conventional techniques.

[0036] Among the above-mentioned trial probabilities, the trial probabilities of movement operations, i.e., translation and rotation, may be set to zero. In this embodiment, the molecular dynamics calculation step described below can simulate behaviors corresponding to the translation and rotation of MOFs and water molecules. Therefore, in the Monte Carlo calculation step, the calculation load can be reduced by setting the probability that translation and rotation trials will be adopted to zero.

[0037] The Monte Carlo calculation can be performed using known Monte Carlo calculation software. The final structure of the MOF and the water molecules arranged in the MOF after a predetermined MC step is completed corresponds to the structure after the simulation using the Monte Carlo method is performed.

[0038] The control unit 11 acquires predicted values ​​of the water adsorption characteristics of the MOF under each partial pressure and temperature (step S15). The water adsorption characteristics may be the amount of water adsorbed (water adsorption amount) or an adsorption isotherm showing the relationship between the water adsorption amount and the partial pressure. The adsorption amount is expressed as the mass (g) of water (adsorbate) adsorbed per 1 g of MOF. The water adsorption amount may include, for example, the water adsorption amount for each MC step, the maximum water adsorption amount among the series of water adsorption amounts, or the average value of the series of water adsorption amounts. The water adsorption amount may be represented by a graph showing the relationship between the water adsorption amount of the MOF and the MC step. The adsorption isotherm is generated by plotting the average water adsorption amount from a predetermined MC step to the final step against each pressure.

[0039] The control unit 11 performs a molecular dynamics simulation on the MOF and water molecule structures after the Monte Carlo simulation (step S16) to calculate the optimal particle arrangement. Molecular dynamics is a method for simulating particle motion by numerically solving Newton's equations of motion. Molecular dynamics calculations can relax the structure of the MOF after water adsorption or desorption, taking into account structural changes in the MOF itself.

[0040] The control unit 11 performs molecular dynamics calculations according to preset calculation conditions. The calculation conditions may include the number of MD steps, ensemble, etc. As the ensemble, the canonical ensemble (NVT ensemble) is preferable. In the canonical ensemble, the number of particles, volume, and temperature of the system are maintained constant during the simulation. The method of the canonical ensemble is not particularly limited, and any appropriate method can be used. Note that the force field acquired in step S13 is also applied to simulations using the molecular dynamics method. In this embodiment, NNP is used as described above. Publicly known molecular dynamics calculation software can be used for the molecular dynamics calculations.

[0041] After executing the simulation using the molecular dynamics method, the control unit 11 increments the number of iterations of the simulation and stores the calculation results of the molecular dynamics calculation in the storage unit 12 (step S17). The calculation results include the number of iterations, the structure of the MOF and the water molecules arranged in the MOF after executing the simulation using the molecular dynamics method (positions of the water molecules), the momentum of the water molecules, etc.

[0042] The control unit 11 determines whether the counted number of repetitions has reached a preset number of repetitions, thereby determining whether to end the simulation (step S18).

[0043] If it is determined that the simulation should not be terminated because the counted number of repetitions is less than the preset number of repetitions (S18: NO), the control unit 11 applies the positions and momentum of the water molecules stored in step S17 as the initial structure in the next cycle (step S19). The control unit 11 returns the process to step S14 and repeats the simulation using the Monte Carlo method and the molecular dynamics method based on the new initial structure. By carrying over the positions and momentum of the water molecules after the molecular dynamics calculation in the current cycle to the next cycle, it is possible to ensure the continuity of the arrangement and direction of movement of the water molecules between cycles.

[0044] If it is determined that the simulation should be terminated because the counted number of repetitions is equal to or greater than the preset number of repetitions (YES in S18), the control unit 11 stores the simulation results in the storage unit 12 (step S20). The control unit 11 stores, for example, the initial structure, intermediate structure, and final structure after the simulation of the MOF, as well as predicted values ​​of the water adsorption properties, in association with each other in the storage unit 12.

[0045] The control unit 11 outputs the result information including the predicted value of the water adsorption characteristics through the output unit 15 (step S21). The control unit 11 displays, for example, information representing the initial structure of the MOF, the amount of water adsorption, and the adsorption isotherm on a display device. Step S21 may be omitted. The control unit 11 then ends the series of processes.

[0046] In the above process, the process of acquiring the water adsorption characteristics in step S15 may be performed after the execution of the simulation using the molecular dynamics method in step S16.

[0047] The prediction device 1 executes the above-described series of processes for each of the multiple MOFs to be simulated, predicting and storing the water adsorption properties of each MOF. This allows the water adsorption properties of multiple MOFs to be candidates for production to be collected. The prediction device 1 then selects MOFs to be produced based on the collected prediction results of the water adsorption properties of each MOF.

[0048] FIG. 3 is a flowchart showing an example of a selection process procedure executed by the prediction device 1.

[0049] The control unit 11 of the prediction device 1 acquires required physical properties for the MOF to be produced (step S31). The required physical properties may include the water adsorption amount. The required water adsorption amount may be the lower limit of the amount of water adsorbed (e.g., 0.2 g / g). The required physical properties may further include upper and lower limits of the partial pressure (e.g., P / P0 of 0.2 to 0.4). The control unit 11 acquires the required physical properties by accepting input from a user, for example, via the input unit 14. The control unit 11 may acquire the required physical properties by receiving information transmitted from an external device connected to the control unit 11 for communication, or by reading out preset required physical properties from the storage unit 12.

[0050] The control unit 11 selects MOFs that satisfy the required physical properties from among the multiple MOFs whose water adsorption properties have been predicted based on the simulation results stored in the memory unit 12 (step S32). Specifically, the control unit 11 extracts MOFs whose predicted water adsorption amount is 0.2 g / g or more when P / P0 is 0.2 to 0.4. The control unit 11 may preferentially select a predetermined number of MOFs in descending order of water adsorption amount.

[0051] The control unit 11 outputs the selection results via the output unit 15 (step S33). The control unit 11 may output information representing the initial structure, water adsorption amount, and adsorption isotherm of each selected MOF in association with the MOF. The control unit 11 then terminates the series of processes.

[0052] In the above-described process, the control unit 11 may acquire the actual measured values ​​of the adsorption / desorption properties of the MOFs selected in step S33, and may perform final selection of only those MOFs whose acquired actual measured values ​​of the adsorption / desorption properties satisfy the required physical properties from among the MOFs selected in step S33.

[0053] In this embodiment, a method for manufacturing a metal-organic framework to which the above-mentioned prediction and selection method is applied can be provided. The method for manufacturing a metal-organic framework of the embodiment includes the steps of: (1) acquiring initial structures of a plurality of metal-organic frameworks; (2) determining, for each metal-organic framework, a configuration of an adsorbate when the adsorbate is adsorbed to the metal-organic framework of the initial structure by simulation using a Monte Carlo method; (3) acquiring predicted values ​​of adsorption properties of a metal-organic framework having a structure corresponding to the determined configuration; (4) selecting a metal-organic framework for which the acquired predicted value of adsorption properties satisfies a predetermined condition; and (5) obtaining the selected metal-organic framework.

[0054] Of the above steps, steps (1) to (4) correspond to the prediction and selection steps described above with reference to Figures 2 and 3. Step (5) corresponds to a step performed by a manufacturing device.

[0055] In the step of obtaining the selected metal organic framework, for example, a hydrothermal synthesis method or a solvothermal synthesis method is used to mix a metal ion source with an organic ligand or a salt thereof to obtain an MOF. For example, an MOF can be obtained by the production method described in JP-A-2013-512223.

[0056] 4A and 4B are diagrams showing examples of predicted and experimental adsorption isotherms. The vertical axis of FIG. 4 represents the amount of adsorbed water molecules (g / g), and the horizontal axis represents the partial pressure P / P0. FIG. 4A shows the adsorption isotherms obtained for MOF1, MOF2, MOF3, MOF4, and MOF5 by the prediction method using the GCMC method of this embodiment, and FIG. 4B shows the experimental adsorption isotherms for the above five types of MOFs. All of the prediction results using the prediction method of this embodiment well reproduce the qualitative trends of the experimental adsorption isotherms, confirming the high prediction accuracy of the method of the present disclosure.

[0057] According to this embodiment, the behavior of the adsorbate and the porous material can be simulated with high accuracy using two types of simulation methods, thereby improving the prediction accuracy of adsorption characteristics. By combining a simulation of the static properties of a substance using the Monte Carlo method and a simulation of the dynamic properties of a substance using the molecular dynamics method to perform structural optimization, it becomes possible to perform a simulation that takes into account the adsorption and desorption of the adsorbate and the movement of the porous material. Since the structure of not only the adsorbate but also the porous material itself may change depending on the adsorption of the adsorbate, the combination of the two methods has the effect of improving prediction accuracy, particularly when predicting flexible substances such as MOFs.

[0058] In simulations, by generating a grand canonical ensemble, it is possible to optimally calculate experimental systems in which an adsorbate is adsorbed inside a porous material. Using NNP as a force field allows for broad application to a variety of substances, making it possible to perform simulations more suited to the purpose of exploring unknown materials. In particular, using NNP to predict the behavior of MOFs containing a variety of elements can improve prediction accuracy.

[0059] By using the position and momentum of the adsorbate after a series of cycles as initial information to simulate the next cycle, it is possible to ensure continuity in the simulation over multiple cycles and improve the reliability of predictions.

[0060] Second Embodiment In the second embodiment, prediction is performed taking into account the volume change caused by the deformation of the MOF. In the second embodiment, differences from the first embodiment will be mainly described, and the same reference numerals will be used to designate components common to the first embodiment, and detailed descriptions thereof will be omitted.

[0061] Some MOFs expand or contract due to the adsorption of an adsorbate. Therefore, it is preferable to consider the volume change associated with the expansion or contraction of the MOF in the simulation. In one embodiment of the present disclosure, in order to consider the volume change of the MOF, the simulation may be performed using the Gibbs Ensemble Monte Carlo (hereinafter also referred to as the GEMC method), which is a Gibbs Ensemble Monte Carlo method.

[0062] The Kibbs ensemble is an extension of the grand canonical ensemble. In the Kibbs ensemble, the system is composed of multiple subsystems, and volume and particles can be exchanged between each subsystem. As in the grand canonical ensemble, the temperature of the system is kept constant throughout the simulation.

[0063] In this embodiment, a first cell representing the MOF system and a second cell representing the system of adsorbates (e.g., water molecules) are generated as a model, and initially, the adsorbates are placed only in the second cell. In the GEMC method, the total volume and total number of particles in all cells are kept constant, and various thermodynamic equilibrium states are reproduced by repeating the stochastic movement (translation and rotation) of the adsorbates, particle exchange between cells, and cell expansion and contraction. The particle exchange operation between cells corresponds to the insertion and deletion of water. In the particle exchange operation, water molecules are placed in the first cell so that the chemical potential of water in both cells ultimately matches.

[0064] A cell scaling operation corresponds to a cell volume exchange. In a scaling operation, one cell is enlarged and the other cell is reduced so that the total volume of the first cell and the second cell remains constant. For example, the prediction device 1 generates random numbers independently for each axial direction (X, Y, and Z directions) of the first cell shown in three dimensions, and expands or reduces the length of each side of the first cell according to the generated random numbers. Based on the volume of the first cell after the expansion or contraction, the prediction device 1 adjusts the length of each side of the second cell so that the sum of the volumes of the first cell and the second cell remains a constant value. The volume of the MOF can be changed by the scaling operation.

[0065] In the GEMC method, calculation conditions including trial probabilities are set, similarly to the GCMC method described in the first embodiment. The trial probabilities in the second embodiment include, for example, the probabilities of a move operation, a particle exchange operation, and a scale operation. By appropriately setting the trial probability of a scale operation, a desired volume change can be performed. The trial probability of a move operation may be zero, similarly to the first embodiment.

[0066] The prediction device 1 executes the prediction process described in the flowchart of Fig. 2 in the same manner as in the first embodiment, except that the GEMC method is used as the Monte Carlo method. The prediction device 1 can generate a volume change of the MOF in the simulation process using the Monte Carlo method in step S14.

[0067] Although the above describes a configuration in which a simulation process including a volume change is performed in the simulation step using the GEMC method, the volume change process may be performed separately. For example, the prediction device 1 may change the volume of the MOF by performing a simulation using the GEMC method while setting the trial probability of operations other than the zoom operation to zero. Thereafter, the prediction device 1 may perform a simulation using the GCMC method, as in the first embodiment.

[0068] According to one embodiment of the GEMC method, volume changes due to expansion or contraction of the MOF are reflected in the simulation, thereby further improving prediction accuracy.

[0069] The following supplementary notes are further disclosed with respect to the above embodiments. (Supplementary Note 1) A prediction method in which a computer executes the steps of: acquiring an initial structure of a metal-organic framework; determining a configuration of an adsorbate when the adsorbate is adsorbed to the metal-organic framework of the initial structure by simulation using a Monte Carlo method; and acquiring a predicted value of the adsorption characteristics of the metal-organic framework having a structure corresponding to the determined configuration. (Supplementary Note 2) The prediction method according to Supplementary Note 1, in which, after determining the configuration of the adsorbate by simulation using the Monte Carlo method, the configuration of the adsorbate is further determined by simulation using a molecular dynamics method. (Supplementary Note 3) The prediction method according to Supplementary Note 2, in which the simulation using the Monte Carlo method and the simulation using the molecular dynamics method are repeatedly performed. (Supplementary Note 4) The prediction method according to Supplementary Note 2 or Supplementary Note 3, in which the simulation using the Monte Carlo method is performed again based on the position or momentum of the adsorbate after the simulation using the molecular dynamics method. (Supplementary Note 5) The prediction method according to any one of Supplementary Notes 1 to 4, wherein the Monte Carlo method is a Gibbs ensemble Monte Carlo method or a grand canonical Monte Carlo method. (Supplementary Note 6) The prediction method according to any one of Supplementary Notes 1 to 5, wherein a volume of the metal organic framework of the initial structure is changed in the simulation using the Monte Carlo method. (Supplementary Note 7) The prediction method according to any one of Supplementary Notes 1 to 6, wherein a volume of the metal organic framework of the initial structure is changed by changing a volume of each simulation cell so that a sum of a volume of the simulation cell of the metal organic framework and a volume of a simulation cell of the adsorbate is constant in the simulation using the Monte Carlo method. (Supplementary Note 8) The prediction method according to any one of Supplementary Notes 1 to 7, wherein a probability that trials of translation and rotation of the adsorbate in the Monte Carlo method are adopted is set to zero. (Supplementary Note 9) The prediction method according to any one of Supplementary Notes 1 to 8, wherein the adsorption characteristics include a maximum adsorption amount or an adsorption isotherm. (Supplementary Note 10) The prediction method according to any one of Supplementary Note 1 to Supplementary Note 9, wherein a simulation using the Monte Carlo method is performed based on a neural network potential as a force field.

[0070] The embodiments of the present disclosure are illustrative in all respects and are not restrictive. The technical features described in each embodiment can be combined with each other. The scope of the present invention includes all modifications within the scope of the claims and equivalents thereto. The sequences shown in each embodiment are not limited, and within the scope of no contradiction, each processing step may be executed in a different order, or multiple processes may be executed in parallel. The entity that performs each process is not limited, and within the scope of no contradiction, the process of each device may be executed by another device.

[0071] 100 Manufacturing system 1 Prediction device 11 Control unit 12 Storage unit 13 Communication unit 14 Input unit 15 Output unit 1P Program 1A Recording medium 2 Manufacturing device

Claims

1. A prediction method in which a computer executes a process of obtaining an initial structure of a metal-organic framework, determining the arrangement of an adsorbate when the adsorbate is adsorbed onto the metal-organic framework of the initial structure by simulation using the Monte Carlo method, and obtaining a predicted value of the adsorption characteristics of the metal-organic framework having a structure corresponding to the determined arrangement.

2. The prediction method according to claim 1, wherein after determining the arrangement of the adsorbate by simulation using the Monte Carlo method, the arrangement of the adsorbate is further determined by simulation using the molecular dynamics method.

3. The prediction method according to claim 2, wherein the simulation using the Monte Carlo method and the simulation using the molecular dynamics method are repeatedly executed.

4. The prediction method according to claim 2 or claim 3, wherein the simulation using the Monte Carlo method is executed again based on the position or momentum of the adsorbate after the simulation using the molecular dynamics method.

5. The prediction method according to any one of claims 1 to 4, wherein the Monte Carlo method is a Gibbs ensemble Monte Carlo method or a grand canonical Monte Carlo method.

6. The prediction method according to any one of claims 1 to 5, wherein in the simulation using the Monte Carlo method, the volume of the metal-organic framework of the initial structure is changed.

7. The prediction method according to any one of claims 1 to 6, wherein in the simulation using the Monte Carlo method, the volume of the metal-organic framework of the initial structure is changed by changing the volume of each simulation cell so that the sum of the volume of the simulation cell of the metal-organic framework and the volume of the simulation cell of the adsorbate becomes constant.

8. The prediction method according to any one of claims 1 to 7, wherein the probability that the translation and rotation trials of the adsorbate in the Monte Carlo method are adopted is set to zero.

9. The prediction method according to any one of claims 1 to 8, wherein the adsorption characteristics include the maximum adsorption amount or the adsorption isotherm.

10. The prediction method according to any one of claims 1 to 9, wherein the simulation using the Monte Carlo method is executed based on a neural network potential as a force field.

11. A computer program that causes a computer to execute a process of obtaining an initial structure of a metal-organic framework, determining an arrangement of an adsorbate when the adsorbate is adsorbed onto the metal-organic framework having the initial structure by simulation using the Monte Carlo method, and obtaining a predicted value of the adsorption characteristics of the metal-organic framework having a structure corresponding to the determined arrangement.

12. A prediction apparatus including a control unit that executes a process of obtaining an initial structure of a metal-organic framework, determining an arrangement of an adsorbate when the adsorbate is adsorbed onto the metal-organic framework having the initial structure by simulation using the Monte Carlo method, and obtaining a predicted value of the adsorption characteristics of the metal-organic framework having a structure corresponding to the determined arrangement.

13. A method for manufacturing a metal-organic framework, including: a step of obtaining initial structures of a plurality of metal-organic frameworks; a step of determining, for each metal-organic framework, an arrangement of an adsorbate when the adsorbate is adsorbed onto the metal-organic framework having the initial structure by simulation using the Monte Carlo method; a step of obtaining a predicted value of the adsorption characteristics of the metal-organic framework having a structure corresponding to the determined arrangement; a step of selecting a metal-organic framework in which the obtained predicted value of the adsorption characteristics satisfies a predetermined condition; and a step of obtaining the selected metal-organic framework.

Images