DATA PROCESSING METHOD, DATA PROCESSING DEVICE, AND DATA PROCESSING SYSTEM

A data processing method acquires a first calculated value by a first calculation method as a physical property value and a second calculated value by a second calculation method as the physical property value, of each of a plurality of first compounds whose true value of the physical property value is known. The method then generates a first correction model and a second correction model for correcting the first calculated value and the second calculated value, respectively, to the true value. The method stores, in a database, corrected values corrected by the first and second correction models of calculated values as the true values, where the calculated values are acquired by the first and second calculation methods as a physical property value of a second compound whose true value of the physical property value is unknown.

FIELD

The present invention relates to a data processing method, a data processing device, and a data processing system.

BACKGROUND OF THE INVENTION

Recently, materials informatics, which combines materials science and data science, is being focused as a method of new materials search. In materials informatics, materials search is conducted by analyzing a database containing various types of information such as structures and physical properties of materials (compounds).

Specifically, in materials informatics, materials search is conducted through data mining and machine learning using a database (see Non Patent Literature 1, for example).

SUMMARY OF THE INVENTION

In order to solve the problem described above and to achieve the goal, in the present invention, the present invention discloses a data processing method comprising:

a first acquisition step of acquiring a first calculated value by a first calculation method as a physical property value of each of a plurality of first compounds, a true value of the physical property value of the first compound being known;

a second acquisition step of acquiring a second calculated value by a second calculation method as the physical property value of each of the first compounds, the second calculation method being able to obtain a calculation result in a region in which the first calculation method is unable to obtain a calculation result;

a generation step of generating a first correction model for correcting the first calculated value to the true value and a second correction model for correcting the second calculated value to the true value;

a third acquisition step of acquiring a third calculated value by the first calculation method as a physical property value of a second compound, a true value of the physical property value of the second compound being unknown;

a first storing step of correcting, by using the first correction model, the third calculated value acquired in a region including a region in which at least the first calculation method is able to obtain a calculation result, and storing the corrected value as the true value of the physical property value of the second compound in a database;

a fourth acquisition step of acquiring a fourth calculated value by the second calculation method as the physical property value of the second compound in a region including a region in which at least the first calculation method is unable to obtain a calculation result; and

a second storing step of correcting the fourth calculated value by using the second correction model, and storing the corrected value as the true value of the physical property value of the second compound in the database.

Further, the present invention discloses a data processing device comprising:

an acquisition unit configured to acquire a first calculated value by a first calculation method as a physical property value of each of a plurality of first compounds, a true value of the physical property value of the first compound being known, and configured to acquire a second calculated value by a second calculation method as the physical property value of the first compound, the second calculation method being able to obtain a calculation result in a region in which the first calculation method is unable to obtain a calculation result;

a generation unit configured to generate a first correction model for correcting the first calculated value to the true value and a second correction model for correcting the second calculated. value to the true value; and

a storage unit configured to correct a third calculated value by using the first correction model, the third calculated value being acquired by the acquisition unit by the first calculation method as a physical property value of a second compound, a true value of the physical property value of the second compound being unknown, the third calculated value being acquired in a region including a region in which at least the first calculation method is able to obtain a calculation result, configured to store the corrected value in a database as the true value of the physical property value of the second compound, configured to correct a fourth calculated value by using the second correction model, the fourth calculated value being acquired by the acquisition unit by the second calculation method as the physical property value of the second compound in a region including a region in which at least the first calculation method is unable to obtain a calculation result, and configured to store the corrected value in the database as the true value of the physical property value of the second compound.

Further, the present invention discloses a data processing system comprising:

an acquisition unit configured to acquire a first calculated value by a first calculation method as a physical property value of each of a plurality of first compounds, a true value of the physical property value of the first compound being known, and configured to acquire a second calculated value by a second calculation method as the physical property value of each of the first compounds, the second calculation method being able to obtain a calculation result in a region in which the first calculation method is unable to obtain a calculation result;

a generation unit configured to generate first correction model for correcting the first calculated value to the true value and a second correction model for correcting the second calculated value to the true value; and

a storage unit configured to correct a third calculated value by using the first correction model, the third calculated value being acquired by the acquisition unit by the first calculation method as a physical property value of a second compound, a true value of the physical property value of the second compound being unknown, the third calculated value being acquired in a region including a region in which at least the first calculation method is able to obtain a calculation result, configured to store the corrected value in a database as the true value of the physical property value of the second compound, configured to correct a fourth calculated value by using the second correction model, the fourth calculated value being acquired by the acquisition unit by the second calculation method as the physical property value of the second compound in a region including a region in which at least the first calculation method is unable to obtain a calculation result, and configured to store the corrected value in the database as the true value of the physical property value of the second compound.

DETAILED DESCRIPTION

The following describes an embodiment of a data processing method, a data processing device, and a data processing system according to the present invention in detail with reference to the accompanying drawings. Specifically, the following describes a system that implements the data processing method as an embodiment disclosed in the present specification.

Embodiment

FIG. 1is a diagram illustrating an example general configuration of a data processing system1according to an embodiment. As illustrated inFIG. 1, the data processing system1according to the embodiment includes a data processing device10and a quantum computing device20. The devices illustrated inFIG. 1can be in direct or indirect communication with each other via a network such as a local area network (LAN) or a wide area network (WAN).

The data processing device10is a von Neumann architecture computer, or what is called a classical computer compared to a quantum computer that implements parallel computing by use of quantum superposition. The data processing' device10illustrated inFIG. 1is, for example, a workstation that can perform density functional theory (DFT) calculations.

The quantum computing device20is implemented by a quantum computer or a quantum annealer. The quantum computing device20is not a quantum computer that implements perfect error correction but a noisy intermediate-scale quantum device (NISQ) that outputs calculation results including noise. The quantum computing device20illustrated inFIG. 1can implement variational quantum eigensolver (VQE), for example.

FIG. 2is a diagram illustrating an example hardware configuration of the, data processing device10. As illustrated in2, the data processing device10includes a central processing unit (CPU)11, a read only memory (ROM)12, a random-access memory (RAM)13, an auxiliary memory14, an input device15, a display device16, and an external I/F17.

The CPU11collectively controls the operations of the data processing device10by executing a computer program to implement functions of the data processing device10. The functions of the data processing device10will be described. later.

The ROM12is a non-volatile memory that stores therein various types of data (information written in the production of the data processing device10) including a computer program for booting the data processing device10. The RAM13is a volatile memory that provides a working area for the CPU11. The auxiliary memory14stores therein various types of data such as the computer program executed by the CPU11. The auxiliary memory14is configured by, for example, a hard disk drive (HDD).

The input device15is a device through which a user performs various types of operations on the data processing device10. The input device15is configured by, for example, a mouse, a keyboard, and a touch panel or a hardware key.

The display device16displays various types of information. The display device16displays, for example, a processing result of the CPU11and a graphical user interface (GUI) through which operations of the user are input. The display device16is, for example, a liquid crystal display, an organic electro luminescence display, or a cathode ray tube display. The display device16may be integrated with the input device15to configure, for example, a touch display.

The external I/F17is an interface for connecting the data processing device with (communicating with) an eternal device such as the quantum computing device20.

FIG. 3is a diagram illustrating example functions of the data processing device10. AlthoughFIG. 3illustrates only the functions relating to the embodiment, the functions of the data processing device10are not limited to these examples. As illustrated inFIG. 3, the data processing device10includes a user interface unit101, a memory unit102, an acquisition unit103, a generation unit104, a storage unit105, and a search unit106.

The user interface unit101has a function of receiving an input from the user and a function of displaying various types of information. The user interface unit101implemented by, for example, the input device15and the display device16illustrated inFIG. 2.

The memory unit102is implemented by, for example, the auxiliary memory14(e.g., HDD) illustrated inFIG. 2. The memory unit102stores therein a database (compound database) that associates compounds with chemical features of the compounds for use in implementing the data processing method according to the embodiment. Specifically, the memory unit102stores therein a database that associates a compound with, for example, a numerical value (molecular descriptor) indicating a feature of a partial structure of the compound and a true value of a physical property value of the compound.

FIG. 4is a diagram illustrating the memory unit inFIG. 3. As illustrated inFIG. 4, for example, the memory unit102stores therein a database102athat contains compounds each associated with a character string (molecular descriptor) indicating a chemical structure, a true value of ionization potential (IP), and a true value of electron affinity (EA). The true values of the physical property values registered. in the database102amay be empirical values obtained experimentally or calculated values obtained by accurate calculation.

As illustrated inFIG. 4, the memory unit102stores therein a trained model102bas data for use in implementing the data processing method according to the embodiment. The trained model102bis used for the processing performed by the search unit106, which will be described later. The trained model102bis generated by machine learning using training data The training data for generating the trained model102bis, for example, a list of compounds whose true values of ionization potential and electron affinity are known as a true value of a physical property value of a physical property A. The training data is, for example, the database102a. The training data may be a database different from the database102abut having the same structure as that of the database102aThe training data may be the database102aand a database different from the database102abut having the same structure as that of the database102a.

When a user inputs a desired physical property value “AX” (an example of a certain feature) of a physical property A, the trained model102bestimates a new compound having the physical property value “AX” and outputs the estimated. result. The physical property A is, for example, ionization potential and electron affinity. When, for example, the trained model102breceives an input of a physical property value “AX” of ionization potential as a physical property value of the physical property A from the search unit106, which will be described later, the trained model102boutputs a character string indicating a chemical structure estimated to have the physical property value “AX”. When the trained model102breceives an input of a physical property value “AX” of electron affinity as a physical property value of the physical property A from the search unit106, which will be described later, the trained model102boutputs a character string indicating a chemical structure estimated to have the physical property value “AX”. When the trained model102breceives an input of a physical property value “AX1” of ionization potential and a physical property value “AX2” of electron affinity as physical property values of the physical property A, the trained model102boutputs a character string indicating a chemical structure estimated to have the physical property value “AX1” and the physical property value “AX2” from the search unit106, which will be described later. The trained model102bmay be generated by the data processing device10or by other devices.

The data processing device10performs a new materials search by using database102aas described above. The number or possible compounds for a new compound is theoretically enormous, whereas the number of compounds registered in the database102awith physical property values is far short of such possible compounds. To increase the amount of information stored in the database102afor use in new materials search, the data processing device10operates with the quantum. computing device20and performs the data processing method to be described below.FIG. 5is a flowchart illustrating example operations of the data processing system according to the embodiment. The following describes the steps of the flowchart.

First, the data processing device10generates a correction model (Step S1). Step S1is performed by the acquisition unit103and the generation unit104illustrated inFIG. 3.

At Step S1, the acquisition unit103acquires a first calculated value by a first calculation method as a physical property value of a plurality of first compounds whose true value of the physical property value is known (first acquisition step). In the embodiment, the first calculation method is LFT described above and is performed by a GFT calculation unit103aincluded in the acquisition unit103. At Step Sl, the acquisition unit103acquires a second calculated value by a second calculation method as a physical property value of the first compounds (second acquisition step). In the embodiment, the second calculation method is VQE described above and is performed when a VQE calculation instruction unit103bincluded in the acquisition unit103sends a computational instruction to the quantum computing device20. At Step S1, the generation unit104generates a first correction model for correcting the first calculated value to the true value and a second correction model for correcting the second calculated value to the true value (generation step). The QE calculation as an example of the second calculation method can obtain a calculation result in a region in which the first calculation method (DFT) cannot obtain a calculation result. Details will be described with reference to FTG.6.FIG. 6illustrates the relation between the DFT calculation result and the true value and the relation between the VQE calculation result and the true value with the horizontal axis indicating the calculated value of ionization potential and the vertical axis indicating the true value of ionization potential. The relations illustrated inFIG. 6are presented for illustrative purposes only in order to explain the concept of the data processing method according to the embodiment, and thus are not always applied to the calculation results of every physical property including the actual calculation results of ionization potential.

As illustrated inFIG. 6, the DFT calculation can obtain a calculation result in a region1000, whereas the DFT calculation cannot obtain a calculation result in a region1100. The region1000is a region in which the DFT calculation can obtain a calculation result correlated with the true value. In other words, the region1000is a region in which the DFT calculation can obtain a correct result qualitatively, or can obtain a valid result.

The region1100includes a region1200in which the DET calculation obtains an invalid calculation result and a region1300in which the DFT calculation is terminated abnormally. Although the DFT calculation can obtain a calculation result in the region1200, the calculation result is less correlated with the true value than the calculation result obtained in the region1000.

The VQE calculation, on the contrary, can obtain a calculation result theoretically in any region, and can obtain a correct calculation result qualitatively. As illustrated inFIG. 6, the VQE calculation performed by the NISQ (quantum computing device20) obtains calculated values that statistically contain a certain level of noise. In other words, the VQE calculation can obtain a correct calculation result qualitatively even if the compound is included in the region1100in which the DFT calculation cannot obtain a calculation result. Compared to the DFT calculation, the VQE calculation takes time. To perform. high-speed processing, the DFT calculation is used for molecules that can be calculated by DFT calculation in collecting data.

In the embodiment, at Step S1, the generation unit104compares the true value with the first calculated value (DFT calculation result) and the second calculated value (VQE calculation result) for each of the first compounds. The generation unit104then generates the first correction model (DFT correction model) in a range in which the first calculated value is correlated with the true value, and generates the second correction model (VQE correction model) in a range in which the second calculated value is correlated with the true value.

Detailed operations of Step S1are described with reference toFIGS. 7 to 9.FIG. 7is a flowchart illustrating detailed operations of Step S1in the flowchart inFIG. 5.FIG. 8is a diagram illustrating the processing performed by the acquisition unit103and the generation unit104inFIG. 7.FIG. 9is a diagram illustrating the processing performed by the generation. unit104in FIG.

As illustrated inFIG. 7, the acquisition unit103acquires a list of first compounds whose true value of a physical property value is known (Step S11). The acquisition unit103acquires data from, for example, the database102astored in the memory unit102. At Step S11, the acquisition unit103may acquire the “list of compounds whose true values of ionization potential and electron affinity are known” from an external device via the user interface unit101.

The DFT calculation unit103agenerates an expression for DFT calculation from the chemical structure of a first compound, and performs the DFT calculation to acquire a calculation result (Step S12). Simultaneously with Step S12, the VQE, calculation instruction unit103bgenerates an expression for VQE calculation from the chemical structure of the first compound and transmits the generated expression to the quantum computing device20to cause the quantum computing device20to perform the VQE calculation and acquires a calculation result (Step S13). Step S12may be performed before Step S13or after Step S13.

FIG. 8is a diagram illustrating an example operation of the acquisition unit103in acquiring calculation results of ionization potential. The upper table inFIG. 8indicates that the DFT calculation obtains a result of “IPD_1” and the VQE calculation obtains a result of “IPV_1” of a compound “CK1” whose true value is “IP_1”. In the same manner, the upper table inFIG. 8indicates that the DFT calculation obtains a result of “IPD_2” and the VQE calculation obtains a result of “IPV_2” of a compound “CK2” whose true value is “IP_2”. The upper table inFIG. 8also indicates that the VQE calculation. obtains a result of “IPV_n” but the DFT calculation has failed to obtain a result or a compound “CKn” whose true value is “IP_n”. In other words, the compound “CKn” is a compound included in the region1300in which the DFT calculation cannot obtain a calculation result.

Referring toFIG. 7, the generation unit104compares the calculation results with the true values (Step S14). At Step S14, the generation unit104determines whether the DFT calculation result is correlated with the true value. In the same manner, at Step S14, the generation unit104determines whether the VQE calculation result is correlated with the true value. In the lower table inFIG. 8, the generation unit104determines that the calculation result “IPD_2” of DFT is not correlated with the true value. This determination indicates that the result obtained by the DFT calculation for the compound “CK2” is included in the region1200. In the example illustrated inFIG. 6, all the results of the VQE calculation are determined to be correlated with the true values.

The generation unit104then. generates the DET correction model and the VQE correction model (Step S15).

The generation unit104generates the DFT correction model for correcting the DPT calculation result to the true value when, for example, the DET calculation result is included in the region1000as illustrated inFIG. 9. The generation unit104generates the VQE correction model for correcting the VQE calculation result to the true value as illustrated inFIG. 9. At Step S1, the DFT calculation and the VQE calculation are performed to generate the DFT correction model and the VQE correction model regarding ionization potential. Regarding electron affinity, the DFT calculation and the VQE calculation are also performed to generate a DFT correction model and a VQE correction model.

Using the DFT correction model and the VQE correction model generated at Step S1enables a wider variety of molecules to be corrected, which will be described below at Step S2.

Referring toFIG. 5, the data processing device10, after Step S1, builds a database (Step S2). Step S2is performed by the acquisition unit103and the storage unit105illustrated inFIG. 3.

At Step S2, the acquisition unit103(DAFT calculation unit103a) acquires a third calculated value by the first calculation method (DFT) as a physical property value of a second compound whose true value of the physical property value is unknown (third acquisition step). The storage unit105corrects, by using the first correction model (DFT correction model), the third calculated value acquired in a region including a region (region1000) in which at least the first calculation method. (LIFT) can obtain a calculation result, and stores the corrected value in the database102aas the true value of the physical property of the second compound (first storing step).

The acquisition unit103(VQE calculation instruction unit103b) acquires, by the second calculation method (VQE), a fourth calculated value in a region including a region (region1100) in which at least the first calculation method cannot obtain a calculation result as a physical property value of the second compound (fourth acquisition step). The storage unit105corrects, by using the second correction model (VQE correction model), the fourth calculated value, and stores, in the database102a, the corrected value as the true value of the physical property value of the second compound (second storing step).

Detailed operations of Step S2are described with reference toFIGS. 10 to 12.FIG. 10is a flowchart illustrating detailed operations of Step S2in the flowchart inFIG. 5.FIG. 11is a diagram illustrating the database after the processing at Step S2.FIG. 12is a diagram illustrating the result of the processing at Step S2.

As illustrated inFIG. 10, the acquisition unit103acquires a list of second compounds whose true value of the physical property value is unknown. (Step S21). For example, the user interface unit101acquires the “list of compounds whose true values of ionization potential and electron affinity are unknown” input by a user, and transmits the list to the acquisition unit103. The number of compounds included in the list of second compounds is greater than the number of compounds included in the list of first compounds. The processes at and after Step S22described below are performed repeatedly for every compound included in the list.

The DFT calculation unit103aperforms the DFT calculation on the second compound (Step S22), and the storage unit105determines whether the DFT calculation has obtained a calculation result (Step S23). Specfically, the storage unit105determines whether the calculation performed by the DFT calculation unit103ais terminated abnormally, or determines whether the calculation result acquired by the DFT calculation unit103ais included in the region1200. In other words, the storage unit105determines whether the calculation result obtained by the DFT calculation is included in the region1000.

The following describes an example determination method. The storage unit105obtains, for example, an approximate function by a first-order approximation for a region in which the DFT calculation results are correlated with the true values that have been known through experiments. When a value obtained by the DFT calculation deviates from a line extrapolated by the approximate function, or when the value is in the region1200, the storage unit105determines that the DFT calculation has failed to obtain a calculation result. The storage unit105also determines whether the value obtained by the DFT calculation is a valid result by using a predetermined threshold corresponding to the subject physical property. For example, in obtaining an IP value by the DFT calculation, it is known that no valid calculation result can be obtained in the region at or below 2 eV. If the value of IP obtained by the DFT calculation is at or below 2 eV, the storage unit105determines that the DFT calculation has failed to obtain a calculation result.

Alternatively, the storage unit105performs stability analysis of solutions to determine whether the DFT calculation has obtained a calculation result. The storage unit105, for example, examines whether any singlet instabilities exist in unrestricted DFT wave functions. If singlet instabilities exist, the storage unit105determines that the DFT calculation has failed to obtain a calculation result. If not, the storage unit105determines that the DFT calculation has obtained a calculation result.

If the DFT calculation obtains a calculation result (Yes at Step S23), the storage unit105corrects the DFT calculation result by using the DFT correction model (Step S24), and stores the corrected value in the database102aas a true value of the physical property value of the second compound (Step S27).

If the DFT calculation has failed to obtain a calculation result (No at Step S23), the VQE calculation instruction unit103bcauses the quantum computing device20to perform the VQE calculation, and acquires a result (Step S25). The storage unit105then corrects the VQE calculation result by using the VQE correction model (Step S26), and stores the corrected value in the database102aas the true value of the physical property value of the second compound (Step S27).

With the processing at Step S2(Steps S21to S27), the true value of ionization potential and the true value of electron affinity can be obtained for many second compounds. The second compounds thus can be registered in the database102aas first compounds as illustrated inFIG. 11, and the amount of information stored in the database102acan be significantly increased. Theoretically, as illustrated inFIG. 12, the database space of molecules can. be increased for both ionization potential and electron affinity by combining a region in which DFT can build a database with a region that can be extended by VQE. The NISQ can output a calculated value correlated with the true value theoretically in any range. However, due to the limitations of hardware resources and computational costs, the use of VQE calculation performed by NISQ is currently limited to a certain range of compounds compared to the DFT calculation performed by the von Neumann architecture computer called a classical computer. At Step S2, the NISQ performs the VQE calculation when the DFT calculation cannot obtain a calculation result, and thus the database can be extended efficiently by using the NISQ.

Referring toFIG. 5, after Step S2, the data processing device10searches the database102astoring an increased amount of information increased at Step S1for a new compound (Step S3). Step S3is performed by the search unit106illustrated inFIG. 3.FIG. 13is a diagram illustrating the operation of the search unit106.

When, for example, a user inputs a desired physical property value “AX” of a physical property A via the input device015to the search unit106, the search unit106inputs the physical property value “AX” to the trained model102bas illustrated inFIG. 13. The trained model102bestimates a structure of a compound X that may possibly have the physical property value “AX” of the physical property A. When, for example, the trained model102breceives, from the search. unit106, a physical property value “AX” of ionization potential as a physical property value of the physical property A, the trained model102boutputs a character string indicating a chemical structure estimated to have the physical property value “AX”. When the trained model102breceives, from the search unit106, a physical property value “AX” of electron affinity as a physical property value of the physical property A, the trained model102boutputs a character string indicating a chemical structure estimated to have the physical property value “AX”. Although not illustrated in the drawings, when the trained model102breceives, from the search unit106, a physical property value “AX1” of ionization potential and a physical property value “AX2” of electron affinity as a physical property value of the physical property A, the trained model102boutputs a character string indicating a chemical structure estimated to have the physical property value “AX1” and the physical property value “AX2”.

According to the embodiment, more efficient new compound search can be achieved by using the database102astoring an increased amount of information increased at Step S2.

Referring toFIG. 5, after Step S3, the data processing device10updates the database102awith the new compounds found at Step S3(Step S4). In the same manner as in Step S2, Step S4is performed by the acquisition unit103and the storage unit105illustrated inFIG. 3.

At Step S4, the acquisition unit103(DFT calculation unit103a) acquires a third calculated value of a new compound by DFT calculation. The storage unit105corrects, by using the first correction model (DFT correction model), the third calculated value acquired in a region including a region (region1000) in which at least the first calculation method. (PFT) can obtain a calculation result, and stores the corrected. value in the database102aas the true value of the physical property value of the new compound.

The acquisition unit103(VQE calculation instruction unit103b) acquires a fourth calculated value by using the second calculation method (VQE) as a physical property value of the new compound in a region including the region (region1100) in which at least the first calculation method cannot obtain a calculation result. The storage unit105corrects the fourth calculated value by using the second correction model (VQE correction model) and stores the corrected value in the database102aas the true value of the physical property value of the new compound.

The following describes detailed operations of Step S4with reference toFIG. 14.FIG. 14is a flowchart illustrating the detailed operations of Step S4in the flowchart inFIG. 5.

As illustrated inFIG. 14, upon receiving a new compound (Step S31), the DFT calculation unit103aperforms the DFT calculation on the new compound (Step S32), and the storage unit105determines whether the DFT calculation has obtained a calculation result (Step S33). If the DFT calculation obtains a calculation result (Yes at Step S33), the storage unit105corrects the DFT calculation result by using the DFT correction model (Step S34), and stores the corrected value in the database102aas a true value of the physical property value of the new compound (Step S37).

If the DFT calculation has failed to obtain a calculation result (No at Step S33), the VQE calculation instruction unit103bcauses the quantum computing device20to perform the VQE calculation, and acquires a calculation result (Step S35). The storage unit105corrects the VQE calculation result by using the VQE correction model (Step S36), and stores the corrected value in the database102aas the true value of the physical property value of the new compound (Step S37).

The true value of a physical property value of a new compound can be acquired by the processing at Step S4, and thus the amount of information stored in the database102acan be further increased. If the true value is only limited to an experimental value not including a calculated value, the true value of the physical property value obtained at Step S4is a predicted true value.

In the embodiment above, the DFT calculation and the VQE calculation are performed on a limited number of first compounds to obtain calculated values of ionization potential and electron affinity. The calculation results are compared with a limited number of true values to obtain correction models for correcting the calculation results that can be applied to a wide variety of molecules. In the embodiment, with the correction models for correcting the calculation results that can be applied to a wide variety of molecules, calculated. values of ionization potential and electron affinity of many second compounds are corrected to the true values. This process according to the embodiment above can increase the amount of information stored in the database102afor use in new materials search.

In the embodiment, using the database102astoring an increased amount of information can achieve more efficient compounds search. For a new compound, the true value of a physical property value can be obtained by using the correction models for calculation results that can be applied to a wide variety of molecules. In this regard, the amount of information stored in the database102acan be further increased.

Although an embodiment of the present invention has been described, the present invention is not limited to the embodiment above and the constituents may be modified without departing from the spirit of the present invention for other embodiments. The constituents of the embodiment above may be combined as appropriate to embody various aspects of the invention. For example, some constituents may be eliminated from the constituents disclosed in the embodiment.

Modifications

The following describes modifications of the embodiment above.

(1) First Modification

In the processing at Step S2the embodiment above, the calculation result (third calculated value) obtained by the DFT calculation in the region1000in which DFT can obtain a (valid) calculation result is corrected by the DFT correction model and stored in the database. In the processing at Step S2, the calculation result (fourth calculated value) obtained by the VQE calculation in the region1100in which DFT cannot obtain a calculation result is corrected by the VOE correction model and stored in the database. In the processing at Step S2, both DFT calculation result (third calculated value) and VQE calculation result (fourth calculated value) are obtained in the region1200in which DFT obtains an invalid calculation result, and the calculation result (fourth calculated value) obtained by the VQE calculation is corrected by the VQE correction model and stored in the database.

The boundary between the region1200and the region1000corresponds to the boundary between a range in which the DFT calculation result is correlated with the true value and a range in which the DFT calculation result is not correlated with the true value. Such a boundary is determined, for example, based. on a threshold set for a correlation coefficient or determined by a user. At or near the boundary, it may be unclear which is more appropriate as a true value, the corrected value of the third calculated value or the corrected value of the fourth calculated value.

The following describes the processing according to the first modification. Suppose that, for example, a DFT calculated value corresponding to the boundary between the region1200and the region1000is set to “A”, and “a” is given as the in al setting or a user setting. In the first modification, if a DFT calculated value “Y1” of ionization potential of a second compound. “Z” satisfies “A−aα≤Y1≤A+α”, the storage unit105causes the display device16to display a corrected value “Y1DFT” that is a corrected. value of “Y1” by the DFT correction model and a corrected. value “Y2VQE.” that is a corrected value of a VQE calculated value “Y2” of the second compound “Z” by the VQE correction model. The storage unit105causes the display device16to display the chemical structure of the second compound “Z”. The user determines which is more appropriate, the corrected value “Y1DFT” or the corrected value “Y2VQE.”, based on the chemical structure, and selects the more appropriate corrected value. The storage unit105stores the corrected value selected by the user in the database102aas the true value of the second compound “Z”.

In the first modification, a DFT calculation result (third calculated value) included in the region1200may be corrected by the DFT correction model and stored in the database. In the first modification, if a DFT calculation result (third calculated value) is included in the region1000and the third calculated value falls within the range described above, the VQE calculation result (fourth calculated value) may be obtained, corrected by the VQE correction model, and stored in the database. The processing according to the first modification can acquire a more appropriate true value of a physical property value in the boundary region.

In the first modification, corrected values are selected by the user, but the corrected values may be selected automatically. In this case, for example, the search unit106performs data mining using the database102aand estimates the value of ionization potential of the second compound “Z”. The storage unit105then stores a value, out of “Y1DFT” and “Y2VQE”, closer to the estimated value of the search unit106in the database102aas the true value of the second compound “Z”.

(2) Second Modification

In the processing at Step S2in the embodiment above, the generation unit104may perform the following processing according to a second modification if a DFT calculation result (third calculated value) of a second compound is corrected by the DFT correction model and stored in the database102aas a true value.

The generation unit104according to the second modification further acquires, via the VOE calculation instruction unit103b, a VQE calculation result of the second compound whose true value has been obtained from the corrected value of the DFT calculation result corrected by the DFT correction model. The generation unit104then generates a second correction model (VQE correction model) by using the value stored as the true value and the VQE calculated value. The second correction model can be updated by optionally performing the processing according to the second modification, which can in turn increase the correction accuracy.

(3) Third Modification

In the embodiment above, the first calculation method is DFT and the second calculation method is VQE, but the calculation methods are not limited to these methods. The first calculation method and the second calculation method used in the data processing method. disclosed in the present specification may be any combination of calculation methods if the combination includes a first calculation method and a second calculation method that can obtain a calculation result in a region in which the first calculation method cannot obtain a calculation result. Examples of the first calculation method include calculation methods based on perturbation theory and coupled cluster theory. Examples of the second calculation method include quantum phase estimation.

(4) Fourth Modification

In the embodiment above, physical property values of ionization potential and electron affinity are calculated, but physical property values of any type may be calculated if the physical property values can be calculated by both first calculation method and second calculation method that can be used in the data processing method disclosed in the present specification.

In the embodiment above, the trained model102blearns the correlation between the chemical structure and IP, the chemical structure and EA, and the chemical structure and IP and EA to estimate a chemical structure having a desired IP, a chemical structure having a desired EA, and a chemical structure having desired IP and PA, but the embodiment is not limited to this. For example, a chemical structure having a third physical property (physical property B) different from IP or EA can be estimated by using the trained model102band a second trained model to be described below. The second trained model is generated by, for example, machine learning using, as training data, a list of compounds whose true value of IP, true value of EA, and true value of the physical property B are known. When the second trained model receives a desired value “BX” of the physical property B from the search unit106, the second trained model outputs an IP value (IPBX) and an EA value (EABX) that may possibly correspond to the physical property value “BX”. The search. unit106then inputs IPBXand EABXto the trained model102band acquires a character string indicating the chemical structure estimated to have the physical property value “BX”. The true values of the physical properties IP and PA of the new compound found in this processing can be obtained by performing the processing at Step S4.

The data processing system1according to the embodiment above includes the data processing device10and the quantum computing device20. The data processing system1, however, may include the functions or the data processing device10and the functions of the quantum computing device20in a distributed manner in a plurality of devices.

For example, the data processing system1may include a database building device including the acquisition unit103, the generation unit104, and the storage unit105, a quantum computing device20, a search device including the trained model102band the search unit106, and a memory device storing the database102a. The database102astored in the memory device may be an integrated database of a plurality of databases built by a plurality of database building devices.

The embodiment above may optionally be combined with the modifications above and the modifications may optionally be combined with one another.

The computer program executed by the data processing device10according to the embodiment above may be recorded and provided in a computer-readable recording medium such as a compact disc read only memory (CD-RCM), a flexible disk (FD), a magneto-optical disk, a compact disc recordable (CD-R), a digital versatile disc (DVD), a Blu-ray disc (registered trademark), and a universal serial bus (USB) memory as an installable or executable file, or may be provided or distributed via a network such as the Internet. The computer program may be embedded and provided in a non-volatile memory such as a ROM.