Plasmonic material exploration method and plasmonic material exploration device

Abstract

To provide a plasmonic material search method and a plasmonic material search device capable of efficiently searching a plasmonic material in a material using a nonconductive material as a mother material.SOLUTION: A plasmonic material search method calculates electromagnetic wave absorption in virtually doping a carrier into a nonconductive material of a first nonconductive material group, generates a prediction model by using information of the nonconductive material of the first nonconductive material group and the electromagnetic wave absorption in virtually doping the carrier into a calculated nonconductive material of the first nonconductive material group, predicts the electromagnetic wave absorption in virtually doping the carrier into a nonconductive material of a second nonconductive material group on the basis of the prediction model, selects a nonconductive material below a threshold preset by the predicted electromagnetic wave absorption in the nonconductive material of the second nonconductive material group as a candidate material, calculates electromagnetic wave absorption in virtually doping the carrier into the candidate material by using a hybrid functional, and selects a nonconductive material with the calculated electromagnetic wave absorption being blow the threshold in the candidate material.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a plasmonic material exploration method and a plasmonic material exploration apparatus. [Background technology]

[0002] Plasmonic materials have been used in a variety of fields, from stained glass in the past to biochemical sensors, detection of lesions such as cancer, near-infrared shielding materials, metamaterials, etc. Plasmonic materials are preferably conductive materials such as metals, alloys, and carrier-doped semiconductors, as they utilize the plasmon resonance characteristic of the material.

[0003] Another important characteristic of plasmonic materials is the degree to which the electric field is enhanced by plasmon resonance in response to incident light.

[0004] Plasmonic materials include metals such as gold and silver, and semiconductors doped with carriers such as antimony-doped tin oxide and tin-doped indium oxide. However, these plasmonic materials have problems such as high cost due to the inclusion of precious or rare metals, and poor weather resistance. Therefore, the discovery of new plasmonic materials is desired.

[0005] One method for searching for novel plasmonic materials is to measure the dielectric function of a material and determine whether it is a plasmonic material from the dielectric function. To maximize the electric field enhancement due to plasmon resonance, materials with low energy loss within the material, i.e., materials with low absorption of irradiated electromagnetic waves, are preferred. Energy loss within a material is described by the imaginary part of the material's dielectric function. Therefore, measuring the dielectric function of candidate plasmonic materials makes it possible to search for plasmonic materials. However, experimentally measuring the dielectric function of all candidate materials using methods such as spectroscopic ellipsometry requires a great deal of effort.

[0006] Another method for exploring novel plasmonic materials is to calculate the dielectric function of a substance using first-principles calculations such as density functional theory (DFT). For example, Non-Patent Document 1 shows that the dielectric function can be calculated with high accuracy by first-principles calculations using hybrid functionals.

[0007] Furthermore, Non-Patent Document 2 shows that plasmonic materials can be searched for in a short period of time from a large number of conductive materials by using machine learning. [Prior art documents] [Non-patent literature]

[0008] [Non-Patent Document 1] S. Yoshio, K. Maki, and K. Adachi, The Journal of Chemical Physics, 144, 234702 (2016). [Non-patent document 2] T. Yoshida, R. Maezono, and K. Hongo, ACS Applied Nano Materials, 4, 1932 (2021). Summary of the Invention [Problem to be solved by the invention]

[0009] One method for exploring plasmonic materials is to determine the dielectric function of conductive materials using first-principles calculations and machine learning.

[0010] However, carrier-doped semiconductors such as antimony-doped tin oxide and tin-doped indium oxide are not considered candidate materials in the first place because their parent materials are nonconductive. Possible methods for doping carriers into nonconductive materials include element substitution, defect introduction, and element insertion into vacancies. However, performing first-principles calculations for all of these cases requires a great deal of effort. Therefore, there was a need for a simple method to explore whether nonconductive materials doped with carriers are suitable for plasmonic materials.

[0011] In view of the problems associated with the above-described conventional techniques, one aspect of the present invention aims to provide a plasmonic material searching method that can efficiently search for plasmonic materials from among substances that use non-conductive materials as parent substances. [Means for solving the problem]

[0012] In order to solve the above problem, according to one aspect of the present invention, Non-conductive materials in the first non-conductive material group by first-principles calculation For the unit cell of a predictive electromagnetic wave absorption calculation step for calculating electromagnetic wave absorption when virtually doped with carriers; a prediction model generating step of generating a prediction model using information on the non-conductive material of the first non-conductive material group and the electromagnetic wave absorption calculated in the prediction electromagnetic wave absorption calculating step when the non-conductive material of the first non-conductive material group is virtually doped with carriers; a prediction step of predicting electromagnetic wave absorption when a non-conductive material of the second non-conductive material group is virtually doped with carriers based on the prediction model; a candidate material selection step of selecting, from the non-conductive materials of the second non-conductive material group, a non-conductive material whose electromagnetic wave absorption predicted in the prediction step is equal to or less than a preset threshold, as a candidate material; an electromagnetic wave absorption calculation step of calculating electromagnetic wave absorption when the candidate material is virtually doped with carriers using a hybrid functional; and a selection step of selecting, from the candidate substances, a non-conductive substance whose electromagnetic wave absorption calculated in the electromagnetic wave absorption calculation step is equal to or less than the threshold value. [Effects of the Invention]

[0013] According to one aspect of the present invention, it is possible to provide a plasmonic material searching method that can efficiently search for a plasmonic material from among substances that use a non-conductive substance as a parent substance. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a flowchart of a plasmonic material searching method according to the first embodiment. [Figure 2] FIG. 2 shows an example of the process of calculating electromagnetic wave absorption for prediction in the first embodiment. [Figure 3] FIG. 3 is a flowchart of a plasmonic material searching method according to the second embodiment. [Figure 4] Figure 4 is an illustration of transfer learning. [Figure 5] FIG. 5 is a diagram showing the hardware configuration of a plasmonic material exploration apparatus according to an embodiment of the present invention. [Figure 6] FIG. 6 is a block diagram showing the functions of a plasmonic material exploration apparatus according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, embodiments for carrying out the present invention will be described, but the present invention is not limited to the following embodiments, and various modifications and substitutions can be made to the following embodiments without departing from the scope of the present invention. [Plasmonic material discovery methods] (1) First embodiment The plasmonic material searching method of this embodiment will be described below with reference to a flowchart 100 in FIG. 1 and a flowchart 200 in FIG.

[0016] Fig. 1 is a flowchart of a plasmonic material exploration method according to the first embodiment. Fig. 2 is an example of a step of calculating electromagnetic wave absorption for prediction according to the first embodiment.

[0017] The plasmonic material searching method according to the first embodiment includes a predictive electromagnetic wave absorption calculation step, a prediction model generation step, a prediction step, a candidate substance selection step, an electromagnetic wave absorption calculation step, and a selection step. (Predictive electromagnetic wave absorption calculation process: S1) In the first embodiment, first, a prediction electromagnetic wave absorption calculation step can be executed.

[0018] The predicted electromagnetic wave absorption calculation step can calculate the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers by first-principles calculation (step S1).

[0019] Here, first-principles calculation refers to a method of calculating the movement of electrons in a material using a computer in accordance with the Schrödinger equation of quantum mechanics. For first-principles calculation, for example, generalized gradient approximation, hybrid functionals, etc. can be used.

[0020] The non-conductive material refers to a material with a band gap larger than 0 eV, and examples thereof include semiconductor materials. The non-conductive material is preferably a wide-gap semiconductor with a band gap in the range of 3 eV±0.2 eV.

[0021] Virtual carrier doping can be performed by increasing (electron doping) or decreasing (hole doping) the number of valence electrons in a material. Virtual carrier doping makes it possible to calculate the electromagnetic wave absorption when carriers are doped into a semiconductor without considering various cases such as element substitution, defect formation, and element insertion into vacancies. More specifically, virtual carrier doping can be performed by adding or removing a predetermined number of electrons to or from the unit lattice of a non-conductive material to be virtually doped.

[0022] In the predictive electromagnetic wave absorption calculation step, first, the crystal structures of N non-conductive materials are obtained from a database storing the crystal structures of M non-conductive materials to be searched for (FIG. 1, step S1). Here, M and N represent the numbers required for model creation. Furthermore, N represents an integer of 2 or greater and smaller than M. The N non-conductive materials are an example of the non-conductive materials in the first non-conductive material group in this embodiment. That is, the M non-conductive materials are non-conductive materials that constitute a material group of non-conductive materials prepared in advance when carrying out the plasmonic material search method of this embodiment. The non-conductive materials in the first non-conductive material group are non-conductive materials that are part of the material group of non-conductive materials prepared in advance.

[0023] The non-conductive materials of the second non-conductive material group, which will be described later, are the remaining non-conductive materials from the non-conductive material group prepared in advance, excluding the non-conductive materials of the first non-conductive material group.

[0024] Specifically, the step of calculating the electromagnetic wave absorption for prediction can be carried out according to the flow shown in FIG. 2, and a first calculation step and a second calculation step can be carried out.

[0025] In the first calculation step, the dielectric function ε when each non-conductive material in the first non-conductive material group is virtually doped with carriers can be calculated by first-principles calculation using hybrid functionals using the N obtained crystal structures (FIG. 2, step S11).

[0026] Next, in the second calculation step, the electromagnetic wave absorption when a non-conductive material in the first non-conductive material group is virtually doped with carriers can be calculated from the imaginary part Im[ε] of the calculated dielectric function ε based on the following formula (1) (FIG. 2, step S12).

[0027] Electromagnetic absorption = ∫Im[ε]dλ (1) Here, electromagnetic wave absorption refers to the value obtained by integrating the imaginary part of the dielectric function with respect to wavelength. In other words, the dielectric function ε is a quantity that depends on the wavelength λ. The wavelength integration range can be selected depending on the purpose. For example, if you want to use a material as a plasmonic material in the visible light wavelength range, the integration range can be set to 380 nm to 780 nm. (Prediction model generation process: S2) Next, a prediction model generation step can be executed. In the prediction model generation step, a prediction model is generated using information on the non-conductive material of the first non-conductive material group and the electromagnetic wave absorption when the non-conductive material of the first non-conductive material group is virtually doped with carriers, calculated in the predictive electromagnetic wave absorption calculation step (FIG. 1, step S2). Here, the generated prediction model is a model that predicts the electromagnetic wave absorption when the non-conductive material is virtually doped with carriers.

[0028] The information on the non-conductive material used to generate the prediction model is not particularly limited, and may include, for example, a weighted average value, maximum value, minimum value, or average value of a certain physical property value of each element contained in the non-conductive material, a radial distribution function of the crystal, or a crystal graph convolutional neural network (CGCNN).

[0029] The method for generating the prediction model is not particularly limited. For example, a prediction model can be generated using information about the non-conductive material in the first non-conductive material group as an explanatory variable and the electromagnetic wave absorption calculated in the step of calculating the predicted electromagnetic wave absorption when the non-conductive material in the first non-conductive material group is virtually doped with carriers as a response variable. For example, neural networks or Gaussian process regression can be used to generate such a prediction model. (Prediction process: S3) Next, a prediction step can be performed. In the prediction step, electromagnetic wave absorption when non-conductive materials in the second non-conductive material group are virtually doped with carriers is predicted based on the prediction model (FIG. 1, step S3). Here, the non-conductive materials in the second non-conductive material group correspond to the non-conductive materials (MN non-conductive materials) obtained by excluding the N non-conductive materials constituting the first non-conductive material group from the M non-conductive materials described above. Therefore, in the prediction step, the prediction model generated in the prediction model generation step is used to predict electromagnetic wave absorption when MN non-conductive materials are virtually doped with carriers. (Candidate substance selection step: S4) Next, a candidate material selection step can be performed. In the candidate material selection step, a non-conductive material from the second non-conductive material group whose electromagnetic wave absorption predicted in the prediction step is equal to or less than a preset threshold is selected as a candidate material (FIG. 1, step S4). Specifically, in the candidate material selection step, it is determined whether the electromagnetic wave absorption when the MN non-conductive materials predicted in the prediction step are virtually doped with carriers is equal to or less than a threshold δ.

[0030] The value of the threshold δ can be changed as appropriate depending on the conditions, but is set to, for example, δ=3.5 in consideration of a preferred plasmonic material.

[0031] In the candidate substance selection process, if the predicted electromagnetic wave absorption is equal to or less than the threshold δ, the process proceeds to the electromagnetic wave absorption calculation process (FIG. 1, step S5) described below. On the other hand, if the predicted electromagnetic wave absorption exceeds the threshold δ, the process returns to the prediction process (FIG. 1, step S3), and the prediction process and the candidate substance selection process can be repeated (FIG. 1, steps S3 to S4). (Electromagnetic wave absorption calculation process: S5) Next, an electromagnetic wave absorption calculation step is executed. In the electromagnetic wave absorption calculation step, the electromagnetic wave absorption when the candidate substance selected in the candidate substance selection step is virtually doped with carriers is calculated using a hybrid functional (FIG. 1, step S5). Specifically, the dielectric function when the candidate substance selected in the candidate substance selection step is virtually doped with carriers is calculated by first-principles calculation using the hybrid functional. Then, the electromagnetic wave absorption when the candidate substance is virtually doped with carriers is calculated from the imaginary part Im[ε] of the calculated dielectric function ε.

[0032] The method for calculating the dielectric function when a candidate substance is virtually doped with carriers by first-principles calculation using a hybrid functional is not particularly limited, and any method can be used. For example, the calculation can be performed in the same manner as in the case of calculating the dielectric function when a non-conductive substance in the first non-conductive substance group is virtually doped with carriers (see FIG. 2, step S11).

[0033] Furthermore, the method for calculating the electromagnetic wave absorption of the candidate material from the imaginary part of the dielectric function is not particularly limited, and any method can be used. For example, the calculation can be performed in the same manner as in the case of calculating the electromagnetic wave absorption when a non-conductive material in the first non-conductive material group is hypothetically doped with carriers (see FIG. 2, step S12). (Selection process: S6) Next, a selection step can be performed. In the selection step, a non-conductive substance whose electromagnetic wave absorption calculated in the electromagnetic wave absorption calculation step is equal to or less than a threshold can be selected from among the candidate substances. The method for selecting a non-conductive substance whose electromagnetic wave absorption is equal to or less than a threshold is not particularly limited, but for example, the calculation can be performed in the same manner as when selecting candidate substances in the candidate substance selection step described above (see FIG. 1, step S4).

[0034] In the selection process, non-conductive materials whose calculated electromagnetic wave absorption is equal to or less than the threshold value δ can be recorded as preferred plasmonic materials. At this time, for example, the crystal structure of the selected non-conductive material, the dielectric function when virtually doped with carriers, and the electromagnetic wave absorption can also be output and recorded.

[0035] On the other hand, if the electromagnetic wave absorption of the candidate substance subjected to the selection process exceeds the threshold value δ, the process returns to the prediction process (FIG. 1, step S3), and the prediction process, candidate substance selection process, electromagnetic wave absorption calculation process, and selection process can be repeated (FIG. 1, steps S3 to S6).

[0036] If the prediction step, candidate material selection step, electromagnetic wave absorption calculation step, and selection step (steps S3 to S6) have been performed for all MN substances, the search for plasmonic materials ends. On the other hand, if steps S3 to S6 have not been performed for all MN substances, the process returns to step S3 and the subsequent steps can be repeated (FIG. 1, steps S3 to S6).

[0037] In the plasmonic material searching method of this embodiment, by performing the above-mentioned predictive electromagnetic wave absorption calculation step, prediction model generation step, prediction step, candidate substance selection step, electromagnetic wave absorption calculation step, and selection step (steps S1 to S6), it becomes possible to search for a plasmonic material from among a large number of non-conductive candidate substances in a short period of time. Therefore, according to this embodiment, it is possible to efficiently search for a plasmonic material whose parent substance is a non-conductive substance.

[0038] Furthermore, in the plasmonic material exploration method of this embodiment, as described above, the electromagnetic wave absorption when a non-conductive material in the first non-conductive material group is virtually doped with carriers is calculated from the imaginary part of the dielectric function when a non-conductive material obtained by first-principles calculation using a hybrid functional is virtually doped with carriers (FIG. 2, steps S11 and S12), thereby making it possible to search for plasmonic materials whose parent material is a non-conductive material with high accuracy from among a large number of candidate materials. (2) Second embodiment The plasmonic material searching method of this embodiment will be described below with reference to a flowchart 300 in FIG.

[0039] FIG. 3 is a flowchart of a plasmonic material searching method according to the second embodiment.

[0040] In the plasmonic material exploration method according to the second embodiment, explanations of parts common to the first embodiment will be omitted. In the second embodiment, the electromagnetic wave absorption for prediction calculation step (S1) can have a first electromagnetic wave absorption for prediction calculation step and a second electromagnetic wave absorption for prediction calculation step.

[0041] In the plasmonic material exploration method according to the second embodiment, the prediction model generation step (S2) can include a first prediction model generation step and a second prediction model generation step. (First predictive electromagnetic wave absorption calculation step: S21) In the first electromagnetic wave absorption calculation step of the electromagnetic wave absorption calculation step, the electromagnetic wave absorption when a non-conductive material in the third non-conductive material group is virtually doped with carriers can be calculated using a generalized gradient approximation (FIG. 3, step S21). As the non-conductive materials in the third non-conductive material group, L non-conductive materials out of M non-conductive materials that are search candidates can be used. L indicates the number required for model generation in the first prediction model generation step described below. Furthermore, L is greater than N and indicates an integer of 2 or greater, and the N non-conductive materials in the first non-conductive material group are included in the third non-conductive material group.

[0042] The generalized gradient approximation is not particularly limited, and for example, a PBE functional (exchange correlation functional) or the like can be used.

[0043] In calculating the electromagnetic wave absorption when a non-conductive material of the third non-conductive material group is virtually doped with carriers, the imaginary part of the dielectric function when a non-conductive material of the third non-conductive material group is virtually doped with carriers can be calculated by first-principles calculation using the generalized gradient approximation instead of the hybrid functional described in the first embodiment. Then, from the imaginary part of the dielectric function, the electromagnetic wave absorption when a non-conductive material of the third non-conductive material group is virtually doped with carriers can be calculated (see FIG. 2 , steps S11 and S12). (Second predictive electromagnetic wave absorption calculation step: S22) In the second predicted electromagnetic wave absorption calculation step of the predicted electromagnetic wave absorption calculation step, a hybrid functional can be used to calculate the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group, which is part of a non-conductive material of the third non-conductive material group, is virtually doped with carriers (Figure 3, step S22).

[0044] As in the first embodiment, the non-conductive materials in the first group of non-conductive materials correspond to the N non-conductive materials obtained from the M non-conductive materials described above, and are part of the non-conductive materials that make up the third group of non-conductive materials.

[0045] The hybrid functional is not particularly limited, and for example, the HSE06 functional or the like can be used.

[0046] In the second predictive electromagnetic wave absorption calculation step, the dielectric function when a non-conductive material of the first non-conductive material group is virtually doped with carriers can be calculated by first-principles calculation using a hybrid functional.

[0047] Then, from the imaginary part of the dielectric function, it is possible to calculate the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers.

[0048] That is, in the second electromagnetic wave absorption calculation step for prediction, the calculation of the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers can be performed from the imaginary part of the dielectric function when a non-conductive material of the first non-conductive material group is virtually doped with carriers, which is obtained by first-principles calculation using a hybrid functional, as in the first embodiment (see FIG. 2 , steps S11 and S12).

[0049] In the plasmonic material exploration method according to the second embodiment, the prediction model generation step can include a first prediction model generation step and a second prediction model generation step. (First prediction model generation step: S23) In the first prediction model generation step of the prediction model generation process, a first prediction model can be generated in which information about the non-conductive materials in the third non-conductive material group is used as an explanatory variable and the electromagnetic wave absorption calculated in the first prediction electromagnetic wave absorption calculation step is used as a target variable.

[0050] The information on the non-conductive materials in the third non-conductive material group used to generate the first prediction model is not particularly limited, and for example, a weighted average of certain physical property values ​​of each element contained in the non-conductive materials in the third non-conductive material group can be used, as in the first embodiment. The method for generating the first prediction model is also not particularly limited, and for example, a neural network or Gaussian process regression can be used, as in the first embodiment. In particular, since a prediction model can be generated with particularly high accuracy, it is preferable to generate the first prediction model using a neural network in the first prediction model generation step. (Second prediction model generation step: S24) In the second prediction model generation step of the prediction electromagnetic wave absorption calculation step, the first prediction model can be transferred to the second prediction model by transfer learning using information on the non-conductive materials in the first non-conductive material group and the electromagnetic wave absorption calculated in the second prediction electromagnetic wave absorption calculation step (Figure 3, step S22).

[0051] The non-conductive materials in the first non-conductive material group correspond to the aforementioned N non-conductive materials. The information on the non-conductive materials used to generate the second prediction model is not particularly limited, and for example, as in the first embodiment, a weighted average value of certain physical property values ​​of the elements contained in the non-conductive materials in the first non-conductive material group can be used.

[0052] Here, transfer learning is a general term for a technique for generating highly accurate models in categories with little data by transferring a predictive model from a category where there is a large amount of data and it is possible to generate highly accurate predictive models to a category where there is only a small amount of data and it is difficult to generate highly accurate predictive models.

[0053] In this embodiment, a first prediction model generated using, for example, a PBE functional as a generalized gradient approximation can be transferred to generate a second prediction model generated using a hybrid functional. Although the dielectric function calculated using the PBE functional does not match well with the measured value, the calculation time is several tenths to several tens of times shorter than that of the hybrid functional, so it is possible to generate a sufficient amount of data to generate a highly accurate prediction model in a short period of time.

[0054] For example, when the first prediction model is a neural network, the transfer method involves transferring a trained neural network model with information about the non-conductive material as an explanatory variable and electromagnetic wave absorption when a non-conductive material is virtually doped with carriers, calculated using a PBE functional, as the objective variable, to the second prediction model generated using the hybrid functional described above.

[0055] Specifically, as shown in Figure 4, in a neural network 10, the parameters (number of layers, number of nodes, weights) of hidden layer 11 are fixed, and a predictor (not shown) is added that uses as input the values ​​output from the last hidden layer 12. This predictor can use, for example, linear regression, decision tree regression, Lasso regression, Ridge regression, Elastic-Net regression, gradient boosting regression, random forest regression, etc. For training the predictor, electromagnetic wave absorption when a non-conductive material is virtually doped with carriers, calculated using a hybrid functional, can be used.

[0056] In addition, when the first prediction model is Gaussian process regression, it is transferred to the second prediction model by Co-kriging and trained with electromagnetic wave absorption calculated by the hybrid functional.

[0057] Using the prediction model (second prediction model) obtained in this way, the prediction process (FIG. 1, step S3) is then carried out, as in the first embodiment, to execute the prediction process, candidate substance selection process, electromagnetic wave absorption calculation process, and selection process, and these processes can be repeated as necessary (see FIG. 1, steps S3 to S6).

[0058] In the plasmonic material discovery method according to the second embodiment, a prediction model that can be used for the discovery of plasmonic materials can be generated from a small number of non-conductive substances by creating a prediction model by transfer learning as described above. Therefore, the plasmonic material discovery method according to this embodiment can discover plasmonic materials whose parent substance is a non-conductive substance in a shorter period of time. [Plasmonic material exploration device] The plasmonic material searching apparatus of this embodiment can have the following predictive electromagnetic wave absorption calculator, predictive model generator, predictor, candidate substance selector, electromagnetic wave absorption calculator, and selector.

[0059] The predictive electromagnetic wave absorption calculation unit can calculate, by first-principles calculation, the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers.

[0060] The prediction model generation unit can generate a prediction model using information on the non-conductive materials in the first non-conductive material group and the electromagnetic wave absorption calculated by the prediction electromagnetic wave absorption calculation unit when the non-conductive materials in the first non-conductive material group are virtually doped with carriers.

[0061] The prediction unit can predict, based on the prediction model, electromagnetic wave absorption when a non-conductive material of the second non-conductive material group is virtually doped with carriers.

[0062] The candidate material selection unit can select, as a candidate material, a non-conductive material from the second non-conductive material group whose electromagnetic wave absorption predicted by the prediction unit is equal to or less than a preset threshold.

[0063] The electromagnetic wave absorption calculation unit can calculate the electromagnetic wave absorption when the candidate material is virtually doped with carriers, using a hybrid functional.

[0064] The selection unit can select, from among the candidate materials, a non-conductive material whose electromagnetic wave absorption calculated by the electromagnetic wave absorption calculation unit is equal to or less than the threshold value.

[0065] As shown in the hardware configuration diagram of Fig. 5, a plasmonic material searching apparatus 50 of this embodiment is configured, for example, as an information processing device (computer), and can be physically configured as a computer system including a CPU (Central Processing Unit: processor) 51, which is an arithmetic processing unit, a RAM (Random Access Memory) 52 and a ROM (Read Only Memory) 53, which are main storage devices, an auxiliary storage device 54, an input / output interface 55, and a display device 56, which is an output device. These are interconnected by a bus 57. The auxiliary storage device 54 and the display device 56 may be provided externally.

[0066] CPU 51 controls the overall operation of plasmonic material exploration apparatus 50 and performs various information processing. CPU 51 executes, for example, the above-described plasmonic material exploration method or a program (plasmonic material exploration program) stored in ROM 53 or auxiliary storage device 54, to calculate a dielectric function or electromagnetic wave absorption, generate a prediction model, and predict electromagnetic wave absorption using the prediction model.

[0067] The RAM 52 is used as a work area for the CPU 51 and may include a non-volatile RAM for storing main control parameters and information.

[0068] The ROM 53 can store a program (plasmonic material search program) and the like.

[0069] The auxiliary storage device 54 is a storage device such as an SSD (Solid State Drive) or HDD (Hard Disk Drive), and can store various data, files, etc. necessary for the operation of the plasmonic material exploration device. Specifically, it can store data on the crystal structure of non-conductive materials, for example.

[0070] The input / output interface 55 includes both a user interface such as a touch panel, keyboard, display screen, and operation buttons, and a communication interface that takes in information from an external data recording server and outputs analysis information to other electronic devices.

[0071] The display device 56 is a monitor display, etc. An analysis screen is displayed on the display device 56, and the screen is updated in response to input / output operations via the input / output interface 55.

[0072] Each function of the plasmonic material exploration device 50 shown in FIG. 5 can be realized by reading a program (plasmonic material exploration program) from a main storage device 54 such as a RAM 52 or a ROM 53 and executing it with a CPU 51, thereby reading and writing data in the RAM 52, etc., and operating an input / output interface 55 and a display device 56.

[0073] Note that the plasmonic material searching apparatus 50 may be configured such that each of the components described below is configured with an individual CPU and associated devices, and the components are connected by cables or the like that can transfer data.

[0074] FIG. 6 shows a functional block diagram of a plasmonic material exploration apparatus 50 of this embodiment.

[0075] 6, plasmonic material searching apparatus 50 can have a receiving unit 61, a processing unit 62, and an output unit 63. These units are realized by software and hardware working together when the CPU executes a pre-stored program, such as the above-described plasmonic material searching method, in an information processing device such as a personal computer equipped with a CPU, a storage device, and various interfaces of plasmonic material searching apparatus 50.

[0076] The configuration of each part will be explained below. (A) Reception The reception unit 61 receives input of commands and data from the user related to the processing executed by the processing device 62. Examples of the reception unit 61 include a keyboard or mouse operated by the user to input commands, a communication device for inputting via a network, and a reading device for inputting from various storage media such as a CD-ROM or DVD-ROM. (B) Processing equipment The processing device 62 can have a predictive electromagnetic wave absorption calculation unit 621, a prediction model generation unit 622, a prediction unit 623, a candidate substance selection unit 624, an electromagnetic wave absorption calculation unit 625, and a selection unit 626. The processing device can also have any other components as necessary. (B-1) Electromagnetic wave absorption calculation unit for prediction The electromagnetic wave absorption calculation unit for prediction 621 can calculate the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers by first-principles calculation, i.e., can perform the above-mentioned electromagnetic wave absorption calculation step for prediction.

[0077] The prediction electromagnetic wave absorption calculator may also be configured to include a first calculator and a second calculator.

[0078] In this case, the first calculation unit can calculate the dielectric function when the non-conductive material of the first non-conductive material group is virtually doped with carriers by first-principles calculation using a hybrid functional. The second calculation unit can calculate the electromagnetic wave absorption when the non-conductive material of the first non-conductive material group is virtually doped with carriers from the imaginary part of the dielectric function calculated by the first calculation unit.

[0079] The electromagnetic wave absorption calculator for prediction may also be configured to include a first electromagnetic wave absorption calculator for prediction and a second electromagnetic wave absorption calculator for prediction.

[0080] In this case, the first predictive electromagnetic wave absorption calculator can use generalized gradient approximation to calculate the electromagnetic wave absorption when the non-conductive material of the third non-conductive material group is virtually doped with carriers.

[0081] In addition, the second predictive electromagnetic wave absorption calculation unit can use a hybrid functional to calculate the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group, which is part of a non-conductive material of the third non-conductive material group, is virtually doped with carriers.

[0082] The second predictive electromagnetic wave absorption calculation unit can calculate the dielectric function when a non-conductive material in the first non-conductive material group is virtually doped with carriers by first-principles calculation using a hybrid functional.

[0083] Then, from the imaginary part of the dielectric function, it is possible to calculate the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers. (B-2) Prediction model generation unit The prediction model generating unit 622 can generate a prediction model using information on the non-conductive materials in the first non-conductive material group and the electromagnetic wave absorption calculated by the prediction electromagnetic wave absorption calculating unit when the non-conductive materials in the first non-conductive material group are virtually doped with carriers, i.e., can perform the prediction model generating step described above.

[0084] In addition, when the prediction electromagnetic wave absorption calculation unit has a first prediction electromagnetic wave absorption calculation unit and a second prediction electromagnetic wave absorption calculation unit, the prediction model generation unit can also have a first prediction model generation unit and a second prediction model generation unit.

[0085] The first prediction model generation unit can generate a first prediction model using information about the non-conductive materials in the third non-conductive material group as an explanatory variable and the electromagnetic wave absorption calculated by the first prediction electromagnetic wave absorption calculation unit as a target variable.

[0086] The second prediction model generation unit can transfer the first prediction model to the second prediction model by transfer learning using information on the non-conductive materials in the first non-conductive material group and the electromagnetic wave absorption calculated by the second prediction electromagnetic wave absorption calculation unit. In this case, the prediction unit (described later) can use the second prediction model as a prediction model. (B-3) Prediction section The prediction unit 623 can predict the electromagnetic wave absorption when carriers are virtually doped into a non-conductive material of the second non-conductive material group based on the prediction model generated by the prediction model generation unit 622. In other words, the prediction process described above can be performed. (B-4) Candidate substance selection section The candidate substance selection unit 624 can select, as a candidate substance, a non-conductive substance from the second non-conductive substance group whose electromagnetic wave absorption predicted by the prediction unit is equal to or less than a preset threshold value. In other words, the candidate substance selection step described above can be performed. (B-5) Electromagnetic wave absorption calculation unit The electromagnetic wave absorption calculation unit 625 can calculate the electromagnetic wave absorption when the candidate material is virtually doped with carriers using a hybrid functional, that is, can perform the electromagnetic wave absorption calculation step described above. (B-6) Selection section The selection unit 626 can select, from among the candidate materials, non-conductive materials whose electromagnetic wave absorption calculated by the electromagnetic wave absorption calculation unit is equal to or less than a threshold value. That is, the above-described selection step can be performed. (C) Output section The output unit 63 may have, for example, a display. The search results obtained by the selection unit 626 can be output to the output unit 63. There are no particular limitations on the content of the search results to be output, but for example, the output unit 63 may output and display, for the non-conductive substance selected by the selection unit 626, the crystal structure of the non-conductive substance, the dielectric function when virtually doped with carriers, and the electromagnetic wave absorption.

[0087] The output unit 63 may be configured to simply record data on the selected non-conductive material or the like in various storage devices, without displaying the search results on a display or the like.

[0088] According to the plasmonic material searching device of this embodiment described above, a search for plasmonic materials whose parent substance is a non-conductive substance can be carried out comprehensively in a short period of time.

[0089] Furthermore, by creating a prediction model by transfer learning as described above, the plasmonic material exploration apparatus of this embodiment can generate a prediction model that can be used to explore plasmonic materials from a small number of non-conductive substances. In this case, the plasmonic material exploration apparatus of this embodiment can explore plasmonic materials whose parent substance is a non-conductive substance in a shorter period of time. [program] Next, the program of this embodiment will be described.

[0090] The program of this embodiment relates to a program for searching for plasmonic materials, and can cause a computer to function as a predictive electromagnetic wave absorption calculation unit, a predictive model generation unit, a prediction unit, a candidate substance selection unit, an electromagnetic wave absorption calculation unit, and a selection unit. (Predictive electromagnetic wave absorption calculation section) The electromagnetic wave absorption calculation unit for prediction can calculate the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers by first-principles calculation, i.e., can perform the electromagnetic wave absorption calculation step for prediction described above.

[0091] The prediction electromagnetic wave absorption calculator may also be configured to include a first calculator and a second calculator.

[0092] In this case, the first calculation unit can calculate the dielectric function when the non-conductive material of the first non-conductive material group is virtually doped with carriers by first-principles calculation using a hybrid functional. The second calculation unit can calculate the electromagnetic wave absorption when the non-conductive material of the first non-conductive material group is virtually doped with carriers from the imaginary part of the dielectric function calculated by the first calculation unit.

[0093] The electromagnetic wave absorption calculator for prediction may also be configured to include a first electromagnetic wave absorption calculator for prediction and a second electromagnetic wave absorption calculator for prediction.

[0094] In this case, the first predictive electromagnetic wave absorption calculator can use generalized gradient approximation to calculate the electromagnetic wave absorption when the non-conductive material of the third non-conductive material group is virtually doped with carriers.

[0095] In addition, the second predictive electromagnetic wave absorption calculation unit can use a hybrid functional to calculate the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group, which is part of a non-conductive material of the third non-conductive material group, is virtually doped with carriers.

[0096] The second predictive electromagnetic wave absorption calculation unit can calculate the dielectric function when a non-conductive material in the first non-conductive material group is virtually doped with carriers by first-principles calculation using a hybrid functional.

[0097] Then, from the imaginary part of the dielectric function, it is possible to calculate the electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers. (Prediction model generation unit) The prediction model generating unit can generate a prediction model using information on the non-conductive materials in the first non-conductive material group and the electromagnetic wave absorption calculated by the prediction electromagnetic wave absorption calculating unit when the non-conductive materials in the first non-conductive material group are virtually doped with carriers, i.e., can perform the prediction model generating step described above.

[0098] In addition, when the prediction electromagnetic wave absorption calculation unit has a first prediction electromagnetic wave absorption calculation unit and a second prediction electromagnetic wave absorption calculation unit, the prediction model generation unit can also have a first prediction model generation unit and a second prediction model generation unit.

[0099] The first prediction model generation unit can generate a first prediction model using information about the non-conductive materials in the third non-conductive material group as an explanatory variable and the electromagnetic wave absorption calculated by the first prediction electromagnetic wave absorption calculation unit as a target variable.

[0100] The second prediction model generation unit can transfer the first prediction model to the second prediction model by transfer learning using information on the non-conductive materials in the first non-conductive material group and the electromagnetic wave absorption calculated by the second prediction electromagnetic wave absorption calculation unit. In this case, the prediction unit (described later) can use the second prediction model as a prediction model. (Prediction Department) The prediction unit can predict the electromagnetic wave absorption when a non-conductive material of the second non-conductive material group is virtually doped with carriers based on the prediction model, that is, can perform the prediction step described above. (Candidate Substance Selection Department) The candidate substance selection unit can select, as a candidate substance, a non-conductive substance from the second non-conductive substance group whose electromagnetic wave absorption predicted by the prediction unit is equal to or less than a preset threshold value, i.e., can perform the candidate substance selection step described above. (Electromagnetic wave absorption calculation section) The electromagnetic wave absorption calculation unit can calculate the electromagnetic wave absorption when the candidate material is virtually doped with carriers using a hybrid functional, i.e., can perform the electromagnetic wave absorption calculation step described above. (Selection section) The selection unit can select, from among the candidate materials, non-conductive materials whose electromagnetic wave absorption calculated by the electromagnetic wave absorption calculation unit is equal to or less than a threshold value, i.e., can perform the above-described selection step.

[0101] The program of this embodiment can be stored in various storage media, such as the RAM and ROM of the plasmonic material exploration apparatus described above, as a main storage device or auxiliary storage device. By loading the program and executing it with a CPU, data can be read and written from and to the RAM, and the input / output interface and display device can be operated and executed. Therefore, the details already described for the plasmonic material exploration apparatus will not be described here.

[0102] The program of the present embodiment described above may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. The program of the present embodiment may also be configured to be provided and distributed via a network such as the Internet.

[0103] The program of this embodiment may be distributed in a state stored on an optical disk such as a CD-ROM or a recording medium such as a semiconductor memory.

[0104] According to the program of this embodiment described above, a comprehensive search for plasmonic materials whose parent substance is a non-conductive substance can be carried out in a short period of time.

[0105] Furthermore, in the program of this embodiment, by creating a predictive model by transfer learning as described above, a predictive model that can be used to search for plasmonic materials can be generated from a small number of non-conductive substances. In this case, the program of this embodiment makes it possible to search for plasmonic materials whose parent substance is a non-conductive substance in a shorter period of time. [Example]

[0106] The present invention will be explained below by giving specific examples, but the present invention is not limited to these examples.

[0107] Hereinafter, plasmonic materials were searched for using the plasmonic material searching device 50 and the plasmonic material searching method described above. As candidates for parent materials of plasmonic materials, a total of 2,329 binary, ternary, and quaternary substances registered in the materials database "Materials Project" as substances with a band gap greater than 0 were prepared. Note that these 2,329 substances correspond to the M non-conductive substances described above.

[0108] The target wavelength was set to visible light, and the integral range of electromagnetic wave absorption was set to 380 nm to 780 nm. The information on the non-conductive material used to create the prediction model was the weighted average, weighted variance, weighted sum, maximum value, and minimum value of the physical properties of each element contained in the composition. For example, for binary material A, wA B wB In this case, the physical property value f is the physical property value f of elements A and B. A , f B It is shown as follows using Weighted average: f ave =(wAf A +wBf B ) / (wA+wB) Weighted variance:f var =[wA(f A -f ave ) 2 +wB(f B -f ave ) 2 ] / (wA+wB) Weighted sum:f sum =wAf A +wBf B Maximum value: f max =max(f A ,f B ) Minimum: f min =min(f A ,fB ) The physical property value f is the period, number of protons, atomic number, atomic radius, atomic radius by Rahm, atomic volume, atomic mass, atomic volume from the Inorganic Crystal Structure Database (ICSD), lattice constant, van der Waals (vdW) radius, vdW radius by Alvarez, vdW radius by Batsanov, vdW radius by Bondi, vdW radius by Dreiding FF, MM3 FF vdW radius, Rowland and Taylor vdW radius, Truhlar vdW radius, UFF vdW radius, Bragg covalent radius, Cerdero covalent radius, Pyykko single bond distance, Pyykko double bond distance, Pyykko triple bond distance, Slater covalent radius, vdW coefficient C6, Gould and Bucko vdW coefficient C6, Density at 295 K, Proton affinity, Dipole polarizability, Electron affinity, Electronegativity, Allen scale electronegativity, Ghosh scale electronegativity, Mulliken scale electronegativity The following data were used: viscosities, DFT band gap, DFT energy, DFT lattice constants of BCC, DFT lattice constants of FCC, DFT magnetic moment, DFT volume, HHI coefficient, specific heat at 20°C, gas-phase basicity, first ionization energy, heat of fusion, heat of formation, molar specific heat capacity, specific heat capacity, heat of vaporization, thermal expansion coefficient, boiling point, Brinell hardness, compressibility, melting point, single bond distance of metallic bond radius, distance of nearest neighbor of metallic bond radius, thermal conductivity at 25°C, speed of sound, Vickers hardness, polarizability, Young's modulus, Poisson's ratio, molar volume, total number of unoccupied electrons, total number of valence electrons, number of unoccupied d electrons, number of d valence electrons, number of unoccupied f electrons, number of f valence electrons, number of unoccupied p electrons, number of p electrons, number of unoccupied s electrons, and number of s valence electrons. The Python library XenonPy was used to calculate information on non-conductive materials.

[0109] Transfer learning using neural networks was selected as a method for creating a predictive model to predict electromagnetic wave absorption when non-conductive materials are virtually doped with carriers.

[0110] Specifically, first, for 1,000 of the M non-conductive materials described above, the electromagnetic wave absorption when virtually doped with carriers was calculated using first-principles calculations using the generalized gradient approximation (first predictive electromagnetic wave absorption calculation step, first predictive electromagnetic wave absorption calculation unit). These 1,000 materials correspond to the L non-conductive materials that make up the previously described third non-conductive material group. Virtual carrier doping was performed by adding one electron to the unit cell of each material.

[0111] The calculations were performed using the Vienna Ab initio Simulation Package (VASP), a plane-wave based first-principles calculation software, with the PBE functional (exchange-correlation functional) in the DFT domain. The PAW (projector augmented wave) potential was used, with a planar cutoff of 520 eV and a k-point density of 0.4 Å. -1 It was decided.

[0112] Using this result, a first prediction model of electromagnetic wave absorption by generalized gradient approximation was created (first prediction model generating step, first prediction model generating unit).

[0113] For N (163) non-conductive materials in the first non-conductive material group out of the L (1000) materials constituting the third non-conductive material group, the electromagnetic wave absorption when virtually doped with carriers was calculated using a hybrid functional (second prediction electromagnetic wave absorption calculation step, second prediction electromagnetic wave absorption calculation unit).

[0114] The calculations were performed using the plane wave basis first-principles calculation software VASP, within the scope of DFT, using the HSE06 functional. The PAW potential was used, with a plane cutoff of 520 eV and a k-point density of 0.6 Å. -1 This calculation is an example of a first-principles calculation using the hybrid functional described above.

[0115] By transfer learning, the first prediction model of electromagnetic wave absorption using generalized gradient approximation was transferred to the second prediction model of electromagnetic wave absorption using hybrid functionals, and the model was trained using data on electromagnetic wave absorption by hybrid functionals for 163 non-conductive materials in the first non-conductive material group (second prediction model generation step, second prediction model generation unit). The Python library XenonPy was used for transfer learning.

[0116] Using the second prediction model as a prediction model, the electromagnetic wave absorption when virtually doped with carriers was predicted for 2166 non-conductive materials in the second non-conductive material group, which are obtained by excluding the N non-conductive materials that make up the first non-conductive material group from the M non-conductive materials (prediction step, prediction unit). Note that the 2166 materials obtained by excluding the N non-conductive materials that make up the first non-conductive material group from the M non-conductive materials correspond to the MN non-conductive materials obtained by excluding the N non-conductive materials (163 materials) from the above-mentioned M non-conductive materials (2329 materials).

[0117] Among the non-conductive materials in the second non-conductive material group, non-conductive materials whose electromagnetic wave absorption predicted in the prediction step is equal to or less than a preset threshold were selected as candidate materials (candidate material selection step, candidate material selection unit). Note that the threshold was set to cesium tungstate (Cs 0.33 The electromagnetic wave absorption of WO3 was used, specifically, the threshold value δ was set to 3.5. In the prediction process, there were 287 substances whose predicted values ​​of electromagnetic wave absorption by the hybrid functional were below the threshold value δ.

[0118] For the above 287 materials, the electromagnetic wave absorption when virtually doped with carriers was calculated using a hybrid functional (electromagnetic wave absorption calculation step, electromagnetic wave absorption calculation unit).

[0119] The calculations were performed using the plane-wave basis first-principles calculation software VASP, using the HSE06 functional in the DFT domain. The PAW potential was used, with a plane cutoff of 520 eV and a k-point density of 0.4 Å. -1This calculation is an example of first-principles calculation using a hybrid functional in the electromagnetic wave absorption calculation section.

[0120] Among the candidate substances, non-conductive substances whose electromagnetic wave absorption calculated in the electromagnetic wave absorption calculation step was below the threshold were selected (selection step, selection unit). The threshold δ was set to δ = 3.5. There were 17 substances whose electromagnetic wave absorption by the hybrid functional was below the threshold. These 17 substances can be said to be suitable materials as plasmonic materials whose parent substance is a non-conductive substance. Examples of plasmonic materials whose parent substance is a non-conductive substance discovered in this example include Mg(SbO3)2, SrZnO2, and SrSnO3.

[0121] This example enabled us to comprehensively search and discover, in a short period of time, from 2,329 substances, substances that could become plasmonic materials in the visible light region by doping with carriers. Furthermore, this example confirmed that the search for plasmonic materials whose parent substance is a non-conductive substance can be carried out comprehensively in a short period of time, thereby shortening the development period. [Explanation of symbols]

[0122] 50 Plasmonic material exploration device 621 Electromagnetic Wave Absorption Calculation Unit for Prediction 622 Prediction Model Generation Unit 623 Prediction Department 624 Candidate Substance Selection Division 625 Electromagnetic Wave Absorption Calculation Unit 626 Selection Section

Claims

1. a predictive electromagnetic wave absorption calculation step of calculating, by first-principles calculation, electromagnetic wave absorption when a unit lattice of a non-conductive material of the first non-conductive material group is virtually doped with carriers; a prediction model generating step of generating a prediction model using information on the non-conductive material of the first non-conductive material group and the electromagnetic wave absorption calculated in the prediction electromagnetic wave absorption calculation step when the non-conductive material of the first non-conductive material group is virtually doped with carriers; a prediction step of predicting electromagnetic wave absorption when a non-conductive material of the second non-conductive material group is virtually doped with carriers based on the prediction model; a candidate material selection step of selecting, from the non-conductive materials of the second non-conductive material group, a non-conductive material whose electromagnetic wave absorption predicted in the prediction step is equal to or less than a preset threshold, as a candidate material; an electromagnetic wave absorption calculation step of calculating electromagnetic wave absorption when the candidate material is virtually doped with carriers using a hybrid functional; a selection step of selecting, from the candidate substances, a non-conductive substance whose electromagnetic wave absorption calculated in the electromagnetic wave absorption calculation step is equal to or less than the threshold.

2. In the step of calculating the electromagnetic wave absorption for prediction, calculating a dielectric function when a non-conductive material of the first non-conductive material group is virtually doped with carriers by first-principles calculation using a hybrid functional; The plasmonic material exploration method according to claim 1 , further comprising calculating electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers from the imaginary part of the dielectric function.

3. The predictive electromagnetic wave absorption calculation step includes: a first predictive electromagnetic wave absorption calculation step of calculating electromagnetic wave absorption when a non-conductive material of the third non-conductive material group is virtually doped with carriers using a generalized gradient approximation; a second predictive electromagnetic wave absorption calculation step of calculating, using a hybrid functional, electromagnetic wave absorption when a non-conductive material of the first non-conductive material group, which is a part of a non-conductive material of the third non-conductive material group, is virtually doped with carriers, The prediction model generation step includes: a first prediction model generating step of generating a first prediction model using information on the non-conductive materials of the third non-conductive material group as an explanatory variable and the electromagnetic wave absorption calculated in the first prediction electromagnetic wave absorption calculating step as a response variable; a second prediction model generation step of transferring the first prediction model to a second prediction model by transfer learning using information on the non-conductive materials of the first non-conductive material group and the electromagnetic wave absorption calculated in the second prediction electromagnetic wave absorption calculation step, The plasmonic material exploration method according to claim 1 , wherein the prediction step uses the second prediction model as the prediction model.

4. The plasmonic material exploration method according to claim 3 , wherein in the first prediction model generating step, the first prediction model is generated using a neural network.

5. In the second predictive electromagnetic wave absorption calculation step, calculating a dielectric function when a non-conductive material of the first non-conductive material group is virtually doped with carriers by first-principles calculation using a hybrid functional; 5. The plasmonic material exploration method according to claim 3, further comprising calculating electromagnetic wave absorption when a non-conductive material of the first non-conductive material group is virtually doped with carriers from the imaginary part of the dielectric function.

6. a prediction electromagnetic wave absorption calculation unit that calculates electromagnetic wave absorption when a unit lattice of a non-conductive material of the first non-conductive material group is virtually doped with carriers by first-principles calculation; a prediction model generating unit that generates a prediction model using information on the non-conductive material of the first non-conductive material group and the electromagnetic wave absorption calculated by the prediction electromagnetic wave absorption calculating unit when the non-conductive material of the first non-conductive material group is virtually doped with carriers; a prediction unit that predicts electromagnetic wave absorption when a non-conductive material of the second non-conductive material group is virtually doped with carriers based on the prediction model; a candidate material selection unit that selects, from the non-conductive materials of the second non-conductive material group, a non-conductive material whose electromagnetic wave absorption predicted by the prediction unit is equal to or less than a preset threshold, as a candidate material; an electromagnetic wave absorption calculation unit that calculates electromagnetic wave absorption when the candidate material is virtually doped with carriers using a hybrid functional; a selection unit that selects, from the candidate materials, a non-conductive material whose electromagnetic wave absorption calculated by the electromagnetic wave absorption calculation unit is equal to or less than the threshold.

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