Dispersibility prediction device, dispersibility prediction method, and dispersibility prediction program

Abstract

To easily obtain the dispersibility of a dispersion liquid without performing an experiment requiring much labor.SOLUTION: The dispersibility prediction device 10 includes a flocculation diameter acquisition part 13 for acquiring a measured flocculation diameter as an experimental flocculation diameter in a particle dispersion liquid in which a prediction target particle that is a particle to be predicted of dispersibility in a particle dispersion liquid in which particles are mixed with a dispersion medium is mixed with a designated dispersion medium that is a designated dispersion medium, a first micro physical property calculation part 14 for calculating a micro physical property that is a predetermined physical property of the prediction target particle based on target particle information representing a predetermined physical property of the prediction target particle and designated dispersion medium information representing a predetermined physical property of the designated dispersion medium, and a dispersibility prediction model construction part 15 for constructing a dispersibility prediction model by setting the micro physical property as an explanatory variable and the flocculation diameter in the particle dispersion liquid as an objective variable.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] One aspect of the present disclosure relates to a variance prediction device, a variance prediction method, and a variance prediction program. [Background technology]

[0002] In a particle dispersion in which particles are mixed in a dispersion medium, the dispersibility is an important index that indicates the properties of the particle dispersion. For example, Patent Document 1 below discloses a method for producing a dispersion of fine particles with excellent dispersibility. A method known as a method for measuring dispersibility is the HSP method. Also known are simulators that predict dispersibility, such as SNAP and KAPSEL. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-281644 Summary of the Invention [Problem to be solved by the invention]

[0004] Currently, there is no technology to predict dispersibility without using experimental values, and dispersibility is predicted by measuring specific physical properties through experiments and performing simulations using the experimental values. In this case, time is required to measure the physical properties, so it has been thought that dispersibility can be obtained more quickly by conducting experiments rather than simulations.

[0005] Therefore, the present invention has been made in consideration of the above problems, and has as its object to easily obtain the dispersibility of a dispersion liquid without carrying out many time-consuming experiments. [Means for solving the problem]

[0006] A dispersibility prediction device according to one aspect of the present disclosure includes an aggregate diameter acquisition unit that acquires, as an experimental aggregate diameter, a measured aggregate diameter in a particle dispersion obtained by mixing prediction target particles, which are particles for which dispersibility in a particle dispersion obtained by mixing particles in a dispersion medium, with a designated dispersion medium, which is a specified dispersion medium; a first micro-physical property calculation unit that calculates micro-physical properties, which are predetermined physical properties in the particle dispersion, based on target particle information that indicates predetermined physical properties of the prediction target particles and designated dispersion medium information that indicates predetermined physical properties of the designated dispersion medium; and a dispersibility prediction model construction unit that constructs a dispersibility prediction model by calculating parameters of a dispersibility prediction model in which the micro-physical properties are explanatory variables and the aggregate diameter in the particle dispersion is a target variable, based on the micro-physical properties calculated by the first micro-physical property calculation unit and the experimental aggregate diameter.

[0007] A dispersibility prediction method according to one aspect of the present disclosure is a dispersibility prediction method executed by a processor, and includes an aggregation diameter acquisition step of acquiring, as an experimental aggregation diameter, a measured aggregation diameter in a particle dispersion obtained by mixing prediction target particles, which are particles for which dispersibility in a particle dispersion is to be predicted, with a designated dispersion medium, which is a designated dispersion medium; a first micro-physical property calculation step of calculating micro-physical properties, which are predetermined physical properties of the particle dispersion, based on target particle information representing predetermined physical properties of the prediction target particles and designated dispersion medium information representing predetermined physical properties of the designated dispersion medium; and a dispersibility prediction model construction step of constructing a dispersibility prediction model by calculating parameters of a dispersibility prediction model in which the micro-physical properties are used as explanatory variables and the aggregation diameter in the particle dispersion as a target variable, based on the micro-physical properties calculated in the first micro-physical property calculation step and the experimental aggregation diameter.

[0008] A dispersibility prediction program according to one aspect of the present disclosure is a dispersibility prediction program for causing a computer to function as a dispersibility prediction device, and causes the computer to execute the following steps: an aggregation diameter acquisition step for acquiring, as an experimental aggregation diameter, a measured aggregation diameter in a particle dispersion obtained by mixing prediction target particles, which are particles whose dispersibility in the particle dispersion is to be predicted, with a designated dispersion medium, which is a designated dispersion medium; a first micro-physical property calculation step for calculating micro-physical properties, which are predetermined physical properties in the particle dispersion, based on target particle information representing predetermined physical properties of the prediction target particles and designated dispersion medium information representing predetermined physical properties of the designated dispersion medium; and a dispersibility prediction model construction step for constructing a dispersibility prediction model by calculating parameters of a dispersibility prediction model in which the micro-physical properties are used as explanatory variables and the aggregation diameter in the particle dispersion as a target variable, based on the micro-physical properties calculated in the first micro-physical property calculation step and the experimental aggregation diameter.

[0009] According to this aspect, a predetermined first micro-physical property that contributes to dispersibility in a particle dispersion can be calculated by molecular simulation based on molecular dynamics calculations and quantum chemical calculations, without the need for experiments, based only on the identification of the target particles and the specified dispersion medium. A minimal experiment, measuring the aggregate diameter in a dispersion in which the target particles are mixed with a specific specified dispersion medium, can obtain explanatory variables and corresponding response variables in a dispersibility prediction model, thereby enabling the calculation of parameters in the dispersibility prediction model. Therefore, a dispersibility prediction model capable of predicting dispersibility can be easily constructed.

[0010] A dispersibility prediction device according to another aspect may further include a second micro-physical property calculation unit that calculates micro-physical properties based on at least target particle information and predicted dispersion medium information, which is physical property information of the prediction dispersion medium that is the dispersion medium to be predicted; an aggregate diameter prediction unit that calculates an aggregate diameter in a particle dispersion in which the target particles to be predicted are mixed with the prediction dispersion medium by applying the micro-physical properties calculated by the second micro-physical property calculation unit to a dispersibility prediction model; and a prediction information output unit that outputs the aggregate diameter calculated by the aggregate diameter prediction unit as dispersibility prediction information.

[0011] A dispersibility prediction method according to another aspect may further include a second micro-physical property calculation step of calculating micro-physical properties based on at least target particle information and predicted dispersion medium information, which is physical property information of the prediction dispersion medium that is the dispersion medium to be predicted; an aggregate diameter prediction step of calculating an aggregate diameter in a particle dispersion in which the target particles to be predicted are mixed with the prediction dispersion medium by applying the micro-physical properties calculated in the second micro-physical property calculation step to a dispersibility prediction model; and a prediction information output step of outputting the aggregate diameter calculated in the aggregate diameter prediction step as dispersibility prediction information.

[0012] A dispersibility prediction program according to another aspect may further execute a second micro-physical property calculation step of calculating micro-physical properties based on at least target particle information and predicted dispersion medium information, which is physical property information of the prediction dispersion medium that is the dispersion medium to be predicted; an aggregate diameter prediction step of calculating an aggregate diameter in a particle dispersion in which the target particles to be predicted are mixed with the prediction dispersion medium by applying the micro-physical properties calculated in the second micro-physical property calculation step to a dispersibility prediction model; and a prediction information output step of outputting the aggregate diameter calculated in the aggregate diameter prediction step as dispersibility prediction information.

[0013] According to this aspect, the micro-physical properties, which are explanatory variables of the constructed dispersibility prediction model, are calculated based on the target particle information and the predicted dispersion medium information of the dispersion medium to be predicted, without the need for experiments. Therefore, by applying the calculated micro-physical properties to the dispersibility prediction model, it is possible to obtain the agglomeration diameter in a particle dispersion prepared using the dispersion medium to be predicted, as dispersibility prediction information.

[0014] In another aspect of the dispersibility prediction device, the micro-physical property may be at least one of the interaction energy between the surface of the particle to be predicted and the dispersion medium, the total free energy of solubility when the dispersion medium dissolves in the dispersion medium, and the dielectric constant of the dispersion medium.

[0015] According to this aspect, physical property values ​​that have a large influence on the dispersibility in a particle dispersion are used as explanatory variables in the dispersibility prediction model, so that a dispersibility prediction model that can predict dispersibility with high accuracy can be constructed.

[0016] In another aspect of the dispersibility prediction device, the specified dispersion medium may be multiple and may include at least a dispersion medium having a dielectric constant belonging to a first range and a dispersion medium having a dielectric constant belonging to a second range that is a range of dielectric constants higher than the first range.

[0017] In view of the fact that the polarity of a dispersion medium significantly contributes to dispersibility and has a strong correlation with the dielectric constant, by specifying a plurality of dispersion mediums with different dielectric constants as the specified dispersion medium, it is possible to widely vary the explanatory variables and the objective variables for parameter calculation, thereby improving the prediction accuracy of the dispersibility prediction model.

[0018] In the dispersibility prediction device according to another aspect, the specified dispersion medium may further include a dispersion medium having a dielectric constant that belongs to a third range that is a range of dielectric constants higher than the second range.

[0019] According to this aspect, by designating a plurality of dispersion media having dielectric constants respectively belonging to the first, second, and third ranges as designated dispersion media, it is possible to more widely vary the explanatory variables and the response variable for parameter calculation, thereby enabling further improvement in the prediction accuracy of the dispersibility prediction model.

[0020] In the dispersibility prediction device according to another aspect, the target particle information may include at least the type and particle size of the predicted target particle.

[0021] According to this aspect, the physical properties of the particles to be predicted are suitably reflected in the calculation of the micro physical properties and the calculation of the parameters of the dispersibility prediction model. [Effects of the Invention]

[0022] According to one aspect of the present disclosure, it is possible to easily obtain the dispersibility of a dispersion liquid without carrying out many time-consuming experiments. [Brief explanation of the drawings]

[0023] [Figure 1] FIG. 2 is a block diagram illustrating an example of a functional configuration of a dispersion prediction device according to an embodiment. [Figure 2] FIG. 1 is a hardware block diagram of a dispersion prediction device according to an embodiment. [Figure 3] FIG. 10 is a diagram showing an example of particle information stored in a particle information storage unit. [Figure 4] 10A and 10B are diagrams showing examples of variations in polarity and dielectric constant of dispersion media in which particles are mixed. [Figure 5] 10 is a diagram showing an example of dispersion medium information stored in a dispersion medium information storage unit. FIG. [Figure 6] 1 is a graph plotting predicted values ​​of aggregate diameter calculated by a dispersibility prediction model against experimental aggregate diameters for each specified dispersion medium. [Figure 7] 10 is a flowchart showing an example of the contents of a variance prediction method in the variance prediction device according to the embodiment. [Figure 8] 10 is a flowchart showing an example of the contents of a variance prediction method in the variance prediction device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0024] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same or equivalent elements are designated by the same reference numerals, and redundant description will be omitted.

[0025] 1 is a block diagram showing an example of the functional configuration of a dispersibility prediction device according to an embodiment. The dispersibility prediction device 10 is a device that predicts the dispersibility of a particle dispersion in which particles are mixed in a dispersion medium. As shown in FIG. 1, the dispersibility prediction device 10 of this embodiment includes a functional unit configured by a processor 101, a particle information storage unit 31, a dispersion medium information storage unit 32, and a dispersibility prediction model storage unit 33.

[0026] Dispersibility prediction device 10 is configured by a computer including processor 101, and functionally includes target particle information acquisition unit 11, designated dispersion medium information acquisition unit 12, aggregate diameter acquisition unit 13, first micro-physical property calculation unit 14, dispersibility prediction model construction unit 15, model output unit 16, predicted dispersion information acquisition unit 17, second micro-physical property calculation unit 18, aggregate diameter prediction unit 19, and predicted information output unit 20. Particle information storage unit 31, dispersion medium information storage unit 32, and dispersibility prediction model storage unit 33 may be configured in dispersibility prediction device 10 as shown in FIG. 1, or may be configured as other devices accessible from dispersibility prediction device 10. Each functional unit and each storage unit will be described later.

[0027] FIG. 2 is a diagram illustrating an example of a hardware configuration of a computer 100 that constitutes the dispersion prediction device 10 according to the embodiment.

[0028] As an example, the computer 100 includes, as hardware components, a processor 101, a main memory device 102, an auxiliary memory device 103, and a communication control device 104. The computer 100 constituting the dispersion prediction device 10 may further include an input device 105 such as a keyboard, a touch panel, a mouse, or the like, and an output device 106 such as a display.

[0029] The processor 101 is a computing device that executes an operating system and application programs. Examples of processors include a central processing unit (CPU) and a graphics processing unit (GPU), but the type of processor 101 is not limited to these. For example, the processor 101 may be a combination of dedicated circuits. The dedicated circuits may be programmable circuits such as field-programmable gate arrays (FPGAs), or other types of circuits.

[0030] The main memory device 102 is a device that stores programs for realizing the variance prediction device 10, etc., and calculation results output from the processor 101. The main memory device 102 may be configured with at least one of a ROM (Read Only Memory) and a RAM (Random Access Memory), for example.

[0031] The auxiliary storage device 103 is generally a device capable of storing a larger amount of data than the main storage device 102. The auxiliary storage device 103 is configured by a non-volatile storage medium such as a hard disk or a flash memory. The auxiliary storage device 103 stores a variance prediction program P1 and various data for causing the computer 100 to function as the variance prediction device 10, etc.

[0032] The communication control device 104 is a device that executes data communication with other computers via a communication network, and may be configured with, for example, a network card or a wireless communication module.

[0033] Each functional element of the variance prediction device 10 is realized by loading a corresponding variance prediction program P1 onto the processor 101 or the main memory device 102 and having the processor 101 execute the program. The variance prediction program P1 includes code for realizing each functional element of the corresponding computer. The processor 101 operates the communication control device 104 in accordance with the program P1 and executes reading and writing of data from and to the main memory device 102 or the auxiliary memory device 103. Through such processing, each functional element of the corresponding computer is realized.

[0034] The dispersion prediction program P1 may be provided by being fixedly recorded on a tangible recording medium such as a CD-ROM, a DVD-ROM, a semiconductor memory, etc. Alternatively, at least one of these programs may be provided via a communication network as a data signal superimposed on a carrier wave.

[0035] 1 again, the functional units of the dispersibility prediction device 10 will be described. The target particle information acquisition unit 11 acquires, based on a specified prediction target particle that is a particle to be predicted for dispersibility, physical property information of the prediction target particle as target particle information.

[0036] Specifically, the target particle information acquisition unit 11 may accept a user's designation of a target particle to be predicted. More specifically, the target particle information acquisition unit 11 may acquire information for identifying the type of the target particle to be predicted based on an input by the user.

[0037] Based on the information identifying the predicted target particle, the target particle information acquisition unit 11 acquires the physical property information of the predicted target particle by referring to the particle information storage unit 31. The particle information storage unit 31 is a storage means for storing particle information.

[0038] FIG. 3 is a diagram showing an example of the configuration of the particle information storage unit 31 and stored data. The particle information includes physical property information of each particle associated with each particle type that identifies the type of particle. The physical property information includes, for example, particle size and parameters (PAR_P1, PAR_P2, PAR_P3,...) that indicate various physical properties. Here, the particle physical property parameters used in molecular simulation based on molecular dynamics calculations include, for example, particle species and particle force field parameters. The force field parameters are parameters necessary for calculating the energy acting on each atom in molecular dynamics calculations. For example, the target particle information acquisition unit 11 acquires, as physical property information, a particle size of "0.5" and physical property parameters "p11, p12, p13,..." based on the identification "silica" that identifies the type of predicted target particle.

[0039] The designated dispersion medium information acquisition unit 12 acquires designated dispersion medium information, which is physical property information of the designated dispersion medium. The designated dispersion medium may be a dispersion medium designated by a user for use in constructing a dispersibility prediction model. The designated dispersion medium information acquisition unit 12 may also receive a specification of a concentration in the particle dispersion liquid.

[0040] The polarity of a dispersion medium has a strong correlation with its dielectric constant, and generally, the higher the polarity, the higher the dielectric constant tends to be. Furthermore, the dielectric constant of a dispersion medium affects the size of the electric double layer formed by the particles, and therefore significantly affects the ease of particle dispersion. Therefore, the ease of particle dispersion varies greatly depending on the polarity of the dispersion medium, so the specified dispersion medium information acquisition unit 12 may receive the specification of multiple dispersion media with different polarities.

[0041] The specified dispersion medium may include a plurality of dispersion mediums having a dielectric constant within a first range and a dispersion medium having a dielectric constant within a second range higher than the first range. By specifying a plurality of dispersion mediums with different dielectric constants, the explanatory variables and objective variables for parameter calculation can be varied over a wide range. This improves the prediction accuracy of the dispersibility prediction model.

[0042] The specified dispersion medium may further include a dispersion medium having a dielectric constant in a third range, which is a range of dielectric constants higher than the second range. By specifying a plurality of dispersion mediums having dielectric constants in the first, second, and third ranges, respectively, as the specified dispersion medium, the explanatory variables and objective variables for parameter calculation can be varied over a wider range. This enables further improvement in the prediction accuracy of the dispersibility prediction model.

[0043] Fig. 4 is a diagram showing examples of classifications and variations of dispersion media according to polarity and dielectric constant. As illustrated in Fig. 4, examples of dispersion media with a dielectric constant of "40 or more" and classified as "high" polarity include water, formic acid, dimethyl sulfoxide, glycerin, and succinonitrile. Examples of dispersion media with a dielectric constant of "10 to 40" and classified as "moderate" polarity include ethylene glycol, ethanol, acetone, methanol, and nitrobenzene. Examples of dispersion media with a dielectric constant of "less than 10" and classified as "low" polarity include benzene, toluene, xylene, chloroform, and hexane.

[0044] In this embodiment, for example, one or more dispersion media classified into low (first range), medium (second range), and high (third range) polarity may be designated as the designated dispersion media.

[0045] The designated dispersion medium information acquisition unit 12 may acquire the designated dispersion medium information by referring to the dispersion medium information storage unit 32. FIG. 5 is a diagram showing an example of data configured and stored in the dispersion medium information storage unit 32. The dispersion medium information includes physical property information of each dispersion medium associated with each type of dispersion medium. The physical property information of the dispersion medium includes parameters (PAR_D1, PAR_D2, PAR_D3,...) that indicate the physical properties of the dispersion medium. Here, the physical property parameters of the dispersion medium used in molecular simulation based on molecular dynamics calculations include, for example, the molecular structure of the dispersion medium and the force field parameters of the dispersion medium. For example, the designated dispersion medium information acquisition unit 12 acquires the associated physical property parameters "d11, d12, d13,..." based on the information of the designated designated dispersion medium "toluene."

[0046] The aggregation diameter acquisition unit 13 acquires, as an experimental aggregation diameter, a measured aggregation diameter in a particle dispersion liquid in which prediction target particles, which are particles whose dispersibility is to be predicted, are mixed in a designated dispersion medium.

[0047] Specifically, particle dispersions are prepared by mixing target particles with each specified dispersion medium, and the agglomeration diameters of the prepared particle dispersions are measured experimentally. Then, the agglomeration diameter acquisition unit 13 acquires the measured agglomeration diameters of each particle dispersion as experimental agglomeration diameters.

[0048] The first micro-physical property calculation unit 14 calculates the micro-physical properties, which are predetermined physical properties of the particle dispersion, based on the target particle information indicating the predetermined physical properties of the prediction target particle and the designated dispersion medium information indicating the predetermined physical properties of the designated dispersion medium. The micro-physical properties are predetermined physical properties that contribute to the dispersibility of the particle dispersion. The first micro-physical property calculation unit 14 may also calculate the micro-physical properties using the concentration of the designated particle dispersion.

[0049] Specifically, the micro-physical property is at least one of the interaction energy between the surface of the predicted target particle and the dispersion medium, the solubility sum free energy when the dispersion medium dissolves in the dispersion medium, and the dielectric constant of the dispersion medium. The micro-physical property may include all of the interaction energy between the surface of the predicted target particle and the dispersion medium, the solubility sum free energy when the dispersion medium dissolves in the dispersion medium, and the dielectric constant of the dispersion medium.

[0050] The interaction energy between the surface of a particle to be predicted and a dispersion medium is a physical property calculated by molecular dynamics calculation, and is the interaction energy at an interface formed by the particle surface and the dispersion medium. This interaction energy reflects the so-called compatibility between the particle surface and the dispersion liquid. The physical property parameters of the particle used in calculating the interaction energy include, for example, the particle species (e.g., "silica") and the force field parameters of the particle (e.g., COMPASS II). The physical property parameters of the dispersion medium used in calculating the interaction energy include, for example, the molecular structure of the dispersion medium (e.g., "acetone") and the force field parameters of the dispersion medium (e.g., COMPASS II).

[0051] The solubility free energy of a dispersion medium when it dissolves in another dispersion medium is a physical property calculated by molecular dynamics calculation, and reflects the strength of the interaction between the dispersion mediums. The physical property parameters of the dispersion medium used to calculate the solubility free energy include, for example, the molecular structure of the dispersion medium (e.g., "acetone") and the force field parameters of the dispersion medium (e.g., GAFF2).

[0052] The dielectric constant of a dispersion medium is a physical property calculated by molecular dynamics calculations and quantum chemical calculations, and is generally expressed as the relative dielectric constant. The dielectric constant of a dispersion medium affects the size of the electric double layer formed by the particles. The physical property parameters of the dispersion medium used to calculate the dielectric constant of a dispersion medium include, for example, the molecular structure of the dispersion medium (e.g., "acetone") and the force field parameters of the dispersion medium (e.g., GAFF2). These micro-physical properties have a large influence on the dispersibility of a particle dispersion, so by using these micro-physical property values ​​as explanatory variables in a dispersibility prediction model, it is possible to construct a dispersibility prediction model that can predict dispersibility with high accuracy.

[0053] The dispersibility prediction model construction unit 15 constructs a dispersibility prediction model by calculating the parameters of the dispersibility prediction model, which uses the microphysical properties as explanatory variables and the agglomeration diameter in the particle dispersion as the target variable, based on the microphysical properties calculated by the first microphysical property calculation unit 14 and the experimental agglomeration diameter.

[0054] Specifically, the dispersibility prediction model construction unit 15 sets a function for calculating the aggregate diameter, which is the response variable, based on the micro physical properties, which are explanatory variables, and parameters constituting coefficients, constant terms, etc. Then, the dispersibility prediction model construction unit 15 applies the micro physical properties calculated by the first micro physical property calculation unit 14 and the experimental aggregate diameter acquired by the aggregate diameter acquisition unit 13 to the set function, and optimizes (fits) the parameters so as to obtain a correlation between the explanatory variables and the response variable, thereby constructing a dispersibility prediction model.

[0055] The dispersibility prediction model constructing unit 15 may use a known statistical analysis technique to construct a dispersibility prediction model based on the microphysical properties calculated by the first microphysical property calculating unit 14 and the experimental agglomeration diameter. Alternatively, the dispersibility prediction model constructing unit 15 may be created using existing statistical analysis software.

[0056] Table 1 below shows examples of data on microscopic physical properties and experimental agglomeration diameters for each dispersion medium (methanol, acetone, acetonitrile, toluene) used to build the dispersibility prediction model. Note that this example is for silica particles with a particle size of 0.5 μm. [Table 1]

[0057] The explanatory variables A to C in Table 1 are the micro physical properties of each dispersion medium calculated by the first micro physical property calculation unit 14, and respectively indicate the following physical properties. Explanatory variable A: Interaction energy between the surface of the particle to be predicted and the dispersion medium Explanatory variable B: The sum of the free energy of dissolution when the dispersion medium dissolves in the dispersion medium Explanatory variable C: Dielectric constant of the dispersion medium The experimental aggregate diameter is the experimental aggregate diameter in each dispersion medium acquired by the aggregate diameter acquisition unit 13.

[0058] As an example, the variance prediction model construction unit 15 sets a function constituting the variance prediction model using parameters x1 to x7 and explanatory variables A, B, and C as follows: Agglomerate diameter=x1+x2×(1 / C)-x3×A+(x4×B)+((1 / C)-x5)×((B-x6)×x7)

[0059] Then, the dispersion prediction model construction unit 15 calculated the following parameters x1 to x7. x1=5132.9229171 x2=1251.4023962 x3=16927.57509 x4=123.18976312 x5=0.1221480043 x6=-7.305626316 x7=4988.9310315

[0060] The aggregation diameters (predicted values) in the rightmost column in Table 1 above are aggregation diameters in each dispersion medium calculated by the dispersibility prediction model constructed by the dispersibility prediction model construction unit 15. Fig. 6 is a diagram showing a plot of the aggregation diameters (predicted values) against the experimental aggregation diameters and their approximation curves. In the example shown in Fig. 6, the coefficient of determination R 2 is a very high value of 0.8568. Therefore, the validity of the variance prediction model constructed by the variance prediction model construction unit 15 based on the example of Table 1 above is very high.

[0061] The model output unit 16 outputs the constructed dispersibility prediction model. Specifically, as an example, the model output unit 16 may store the dispersibility prediction model in the dispersibility prediction model storage unit 33. The dispersibility prediction model storage unit 33 is a storage means for storing the constructed dispersibility prediction model. By storing the constructed dispersibility prediction model, it can be used to predict dispersibility in a particle dispersion in which the prediction target particles are mixed in a dispersion medium.

[0062] In addition, when the dispersibility prediction device 10 is a device for generating a dispersibility prediction model, the dispersibility prediction device 10 does not need to include the predicted dispersion information acquisition unit 17, the second micro-physical property calculation unit 18, the aggregate diameter prediction unit 19, and the predicted information output unit 20. In addition, when the dispersibility prediction device 10 is a device for predicting the dispersibility of a particle dispersion using a constructed dispersibility prediction model, the dispersibility prediction device 10 does not need to include the target particle information acquisition unit 11, the designated dispersion medium information acquisition unit 12, the aggregate diameter acquisition unit 13, the first micro-physical property calculation unit 14, the dispersibility prediction model construction unit 15, and the model output unit 16.

[0063] Next, a functional unit for predicting dispersibility using a dispersibility prediction model will be described. The predicted dispersion information acquisition unit 17 acquires physical property information of the predicted dispersion medium, which is the dispersion medium to be predicted, as predicted dispersion medium information. Specifically, the predicted dispersion information acquisition unit 17 acquires the physical property information of the predicted dispersion medium based on the specified predicted dispersion medium by referring to the dispersion medium information storage unit 32. Note that the predicted dispersion information acquisition unit 17 may further acquire the concentration of the particles to be predicted in the particle dispersion.

[0064] The second micro-physical property calculation unit 18 calculates micro-physical properties based on at least the target particle information and the predicted dispersion medium information, which is physical property information of the predicted dispersion medium, in the same manner as the first micro-physical property calculation unit 14. Specifically, the second micro-physical property calculation unit 18 calculates micro-physical properties corresponding to the explanatory variables in the constructed dispersibility prediction model. That is, the second micro-physical property calculation unit 18 calculates at least one of the interaction energy between the surface of the prediction target particle and the dispersion medium, the solubility sum free energy when the dispersion medium dissolves in the dispersion medium, and the dielectric constant of the dispersion medium.

[0065] The agglomeration diameter prediction unit 19 calculates the agglomeration diameter in a particle dispersion in which the particles to be predicted are mixed with a prediction dispersion medium by applying the micro-physical properties calculated by the second micro-physical property calculation unit 18 to a dispersibility prediction model.

[0066] The prediction information output unit 20 outputs the aggregate diameter calculated by the aggregate diameter prediction unit 19 as dispersibility prediction information. The output mode is not limited, and the prediction information output unit 20 may store the dispersibility prediction information in a predetermined storage means, display it on a predetermined display means, or transmit it to a predetermined device.

[0067] Next, a description will be given of a dispersion prediction method executed by the dispersion prediction device 10 in this embodiment. Figures 7 and 8 are flowcharts showing an example of the contents of the dispersion prediction method in the dispersion prediction device 10. The dispersion prediction method is executed by loading a dispersion prediction program P1 into the processor 101 and executing the program to realize each of the functional units 11 to 20.

[0068] 7 is a flowchart showing the processing steps of the dispersibility prediction method in the phase of constructing a dispersibility prediction model. In step S1, the target particle information acquisition unit 11 acquires physical property information of the target particle as target particle information based on the designation of the target particle. In step S2, the designated dispersion medium information acquisition unit 12 acquires designated dispersion medium information, which is physical property information of the designated dispersion medium, based on the designation of the dispersion medium.

[0069] In step S3, an experiment is performed to measure the agglomeration diameter in a particle dispersion in which target particles are mixed in a designated dispersion medium. In step S4, the agglomeration diameter acquisition unit 13 acquires the measured agglomeration diameter in the particle dispersion in which prediction target particles are mixed in a designated dispersion medium as the experimental agglomeration diameter.

[0070] In step S5, the first micro-physical property calculation unit 14 calculates the micro-physical properties, which are predetermined physical properties of the particle dispersion, based on the target particle information and the specified dispersion medium information. The micro-physical properties are at least one of the interaction energy between the predicted target particle surface and the dispersion medium, the solubility sum free energy when the dispersion medium dissolves in the dispersion medium, and the dielectric constant of the dispersion medium.

[0071] In step S6, the dispersibility prediction model construction unit 15 calculates parameters in the functions constituting the dispersibility prediction model by fitting based on the microphysical properties calculated by the first microphysical property calculation unit 14 and the experimental agglomeration diameter. In step S7, the dispersibility prediction model construction unit 15 constructs a dispersibility prediction model based on the calculated parameters. In step S8, the model output unit 16 outputs the constructed dispersibility prediction model.

[0072] 8 is a flowchart showing the processing steps of the dispersibility prediction method in the phase of predicting dispersibility using a dispersibility prediction model. In step S11, the predicted dispersion information acquisition unit 17 acquires predicted dispersion medium information based on a specification of a predicted dispersion medium that is a dispersion medium to be predicted. In step S12, the second micro-physical property calculation unit 18 calculates predetermined micro-physical properties corresponding to the explanatory variables of the dispersibility prediction model based on the target particle information and the predicted dispersion medium information.

[0073] In step S13, the aggregate diameter prediction unit 19 calculates the aggregate diameter in a particle dispersion in which the particles to be predicted are mixed with a prediction dispersion medium by applying the micro-physical properties calculated in step S12 to a dispersibility prediction model. The prediction information output unit 20 outputs the calculated aggregate diameter in step S14 as dispersibility prediction information.

[0074] According to the dispersibility prediction device 10, dispersibility prediction method, and dispersibility prediction program P1 of the present embodiment described above, a predetermined first micro-physical property that contributes to the dispersibility of a particle dispersion can be calculated by molecular simulation based on molecular dynamics calculations and quantum chemical calculations, without the need for experiments, based only on the identification of the target particles and a specified dispersion medium. A minimal experiment, measuring the aggregate diameter in a dispersion in which the target particles are mixed with a specific specified dispersion medium, can obtain explanatory variables and corresponding response variables in a dispersibility prediction model, making it possible to calculate parameters in the dispersibility prediction model. Therefore, a dispersibility prediction model capable of predicting dispersibility can be easily constructed.

[0075] The present invention has been described in detail above based on the embodiments. However, the present invention is not limited to the above embodiments. Various modifications of the present invention are possible without departing from the spirit and scope of the present invention.

[0076] The gist of the present disclosure is as follows [1] to

[10] .

[0077] [1] an aggregation diameter acquiring unit that acquires, as an experimental aggregation diameter, a measured aggregation diameter in a particle dispersion obtained by mixing prediction target particles, which are particles whose dispersibility is to be predicted in a particle dispersion obtained by mixing particles in a dispersion medium, with a designated dispersion medium; a first micro-physical property calculation unit that calculates micro-physical properties, which are predetermined physical properties of the particle dispersion, based on target particle information that indicates predetermined physical properties of the predicted target particle and designated dispersion medium information that indicates predetermined physical properties of the designated dispersion medium; a dispersibility prediction model constructing unit that constructs the dispersibility prediction model by calculating parameters of a dispersibility prediction model in which the micro physical properties are used as explanatory variables and an aggregation diameter in the particle dispersion is used as a response variable based on the micro physical properties calculated by the first micro physical property calculating unit and the experimental aggregation diameter; A dispersion prediction device comprising:

[0078] [2] a second micro-physical property calculation unit that calculates the micro-physical properties based on at least the target particle information and predicted dispersion medium information, which is physical property information of a predicted dispersion medium that is a dispersion medium to be predicted; an aggregate diameter prediction unit that calculates an aggregate diameter in a particle dispersion in which the prediction target particles are mixed in the prediction dispersion medium by applying the micro-physical properties calculated by the second micro-physical property calculation unit to the dispersibility prediction model; a prediction information output unit that outputs the aggregate diameter calculated by the aggregate diameter prediction unit as dispersibility prediction information; The dispersion prediction device according to [1], further comprising:

[0079] [3] The micro-physical property is at least one of an interaction energy between the surface of the prediction target particle and the dispersion medium, a total free energy of solubility when the dispersion medium dissolves in the dispersion medium, and a dielectric constant of the dispersion medium. The dispersion prediction device according to [1] or [2].

[0080] [4] The specified dispersion medium is a plurality of dispersion media, and includes at least a dispersion medium having a dielectric constant belonging to a first range and a dispersion medium having a dielectric constant belonging to a second range that is a range of dielectric constant higher than the first range. The dispersion prediction device according to any one of [1] to [3].

[0081] [5] The specified dispersion medium further includes a dispersion medium having a dielectric constant belonging to a third range that is a range of dielectric constants higher than the second range. [4] The dispersion prediction device according to [4].

[0082] [6] the target particle information includes at least the type and particle size of the predicted target particle; The dispersion prediction device according to any one of [1] to [5].

[0083] [7] 1. A processor-implemented method for variance prediction, comprising: an aggregation diameter acquisition step of acquiring, as an experimental aggregation diameter, a measured aggregation diameter in a particle dispersion obtained by mixing prediction target particles, which are particles whose dispersibility in the particle dispersion is to be predicted, with a designated dispersion medium, which is a designated dispersion medium; a first micro-physical property calculation step of calculating micro-physical properties, which are predetermined physical properties of the particle dispersion, based on target particle information indicating predetermined physical properties of the predicted target particle and designated dispersion medium information indicating predetermined physical properties of the designated dispersion medium; a dispersibility prediction model construction step of constructing the dispersibility prediction model by calculating parameters of a dispersibility prediction model in which the micro physical properties are used as explanatory variables and the aggregation diameter in the particle dispersion is used as a response variable based on the micro physical properties calculated in the first micro physical property calculation step and the experimental aggregation diameter; A variance prediction method having:

[0084] [8] a second micro-physical property calculation step of calculating the micro-physical properties based on at least the target particle information and predicted dispersion medium information, which is physical property information of a predicted dispersion medium that is a dispersion medium to be predicted; an aggregation diameter prediction step of calculating an aggregation diameter in a particle dispersion obtained by mixing the prediction target particles in the prediction dispersion medium by applying the micro-physical properties calculated in the second micro-physical property calculation step to the dispersibility prediction model; a prediction information output step of outputting the aggregate diameter calculated in the aggregate diameter prediction step as dispersibility prediction information; The dispersion prediction method according to [7], further comprising:

[0085] [9] A dispersion prediction program for causing a computer to function as a dispersion prediction device, The computer, an aggregation diameter acquisition step of acquiring, as an experimental aggregation diameter, a measured aggregation diameter in a particle dispersion obtained by mixing prediction target particles, which are particles whose dispersibility in the particle dispersion is to be predicted, with a designated dispersion medium, which is a designated dispersion medium; a first micro-physical property calculation step of calculating micro-physical properties, which are predetermined physical properties of the particle dispersion, based on target particle information indicating predetermined physical properties of the predicted target particle and designated dispersion medium information indicating predetermined physical properties of the designated dispersion medium; a dispersibility prediction model construction step of constructing the dispersibility prediction model by calculating parameters of a dispersibility prediction model in which the micro physical properties are used as explanatory variables and the aggregation diameter in the particle dispersion is used as a response variable based on the micro physical properties calculated in the first micro physical property calculation step and the experimental aggregation diameter; A dispersion prediction program that executes the above.

[0086]

[10] a second micro-physical property calculation step of calculating the micro-physical properties based on at least the target particle information and predicted dispersion medium information, which is physical property information of a predicted dispersion medium that is a dispersion medium to be predicted; an aggregation diameter prediction step of calculating an aggregation diameter in a particle dispersion obtained by mixing the prediction target particles in the prediction dispersion medium by applying the micro-physical properties calculated in the second micro-physical property calculation step to the dispersibility prediction model; a prediction information output step of outputting the aggregate diameter calculated in the aggregate diameter prediction step as dispersibility prediction information; The dispersibility prediction program according to [9], further comprising: [Explanation of symbols]

[0087] 10...dispersibility prediction device, 11...target particle information acquisition unit, 12...designated dispersion medium information acquisition unit, 13...aggregate diameter acquisition unit, 14...first micro-physical property calculation unit, 15...dispersibility prediction model construction unit, 16...model output unit, 17...predicted dispersion liquid information acquisition unit, 18...second micro-physical property calculation unit, 19...aggregate diameter prediction unit, 20...prediction information output unit, 31...particle information storage unit, 32...dispersion medium information storage unit, 33...dispersibility prediction model storage unit, P1...dispersibility prediction program.

Claims

1. an aggregation diameter acquiring unit that acquires, as an experimental aggregation diameter, a measured aggregation diameter in a particle dispersion obtained by mixing prediction target particles, which are particles whose dispersibility is to be predicted in a particle dispersion obtained by mixing particles in a dispersion medium, with a designated dispersion medium; a first micro-physical property calculation unit that calculates micro-physical properties, which are predetermined physical properties of the particle dispersion, based on target particle information that indicates predetermined physical properties of the predicted target particle and designated dispersion medium information that indicates predetermined physical properties of the designated dispersion medium; a dispersibility prediction model constructing unit that constructs the dispersibility prediction model by calculating parameters of a dispersibility prediction model in which the micro physical properties are used as explanatory variables and an aggregation diameter in the particle dispersion is used as a response variable based on the micro physical properties calculated by the first micro physical property calculating unit and the experimental aggregation diameter; A dispersion prediction device comprising:

2. a second micro-physical property calculation unit that calculates the micro-physical properties based on at least the target particle information and predicted dispersion medium information, which is physical property information of a predicted dispersion medium that is a dispersion medium to be predicted; an aggregation diameter prediction unit that calculates an aggregation diameter in a particle dispersion in which the prediction target particles are mixed in the prediction dispersion medium by applying the micro-physical properties calculated by the second micro-physical property calculation unit to the dispersibility prediction model; a prediction information output unit that outputs the aggregate diameter calculated by the aggregate diameter prediction unit as dispersibility prediction information; The dispersion prediction device according to claim 1 , further comprising:

3. The micro-physical property is at least one of an interaction energy between the surface of the prediction target particle and the dispersion medium, a total free energy of solubility when the dispersion medium dissolves in the dispersion medium, and a dielectric constant of the dispersion medium. The dispersion prediction device according to claim 1 or 2.

4. the specified dispersion medium is a plurality of dispersion media, and includes at least a dispersion medium having a dielectric constant belonging to a first range and a dispersion medium having a dielectric constant belonging to a second range that is a range of dielectric constant higher than the first range; The dispersion prediction device according to claim 1 .

5. the specified dispersion medium further includes a dispersion medium having a dielectric constant belonging to a third range that is a range of dielectric constants higher than the second range; The dispersion prediction device according to claim 4 .

6. the target particle information includes at least the type and particle size of the predicted target particle; The dispersion prediction device according to claim 1 .

7. 1. A processor-implemented method for variance prediction, comprising: an aggregation diameter acquisition step of acquiring, as an experimental aggregation diameter, a measured aggregation diameter in a particle dispersion obtained by mixing prediction target particles, which are particles whose dispersibility in the particle dispersion is to be predicted, with a designated dispersion medium, which is a designated dispersion medium; a first micro-physical property calculation step of calculating micro-physical properties, which are predetermined physical properties of the particle dispersion, based on target particle information indicating predetermined physical properties of the predicted target particle and designated dispersion medium information indicating predetermined physical properties of the designated dispersion medium; a dispersibility prediction model construction step of constructing the dispersibility prediction model by calculating parameters of a dispersibility prediction model in which the micro physical properties are used as explanatory variables and the aggregation diameter in the particle dispersion is used as a response variable based on the micro physical properties calculated in the first micro physical property calculation step and the experimental aggregation diameter; A variance prediction method having:

8. a second micro-physical property calculation step of calculating the micro-physical properties based on at least the target particle information and predicted dispersion medium information, which is physical property information of a predicted dispersion medium that is a dispersion medium to be predicted; an aggregation diameter prediction step of calculating an aggregation diameter in a particle dispersion obtained by mixing the prediction target particles in the prediction dispersion medium by applying the micro-physical properties calculated in the second micro-physical property calculation step to the dispersibility prediction model; a prediction information output step of outputting the aggregate diameter calculated in the aggregate diameter prediction step as dispersibility prediction information; The method of claim 7 further comprising:

9. A dispersion prediction program for causing a computer to function as a dispersion prediction device, The computer, an aggregation diameter acquisition step of acquiring, as an experimental aggregation diameter, a measured aggregation diameter in a particle dispersion obtained by mixing prediction target particles, which are particles whose dispersibility in the particle dispersion is to be predicted, with a designated dispersion medium, which is a designated dispersion medium; a first micro-physical property calculation step of calculating micro-physical properties, which are predetermined physical properties of the particle dispersion, based on target particle information indicating predetermined physical properties of the predicted target particle and designated dispersion medium information indicating predetermined physical properties of the designated dispersion medium; a dispersibility prediction model construction step of constructing the dispersibility prediction model by calculating parameters of a dispersibility prediction model in which the micro physical properties are used as explanatory variables and the aggregation diameter in the particle dispersion is used as a response variable based on the micro physical properties calculated in the first micro physical property calculation step and the experimental aggregation diameter; A dispersion prediction program that executes the above.

10. a second micro-physical property calculation step of calculating the micro-physical properties based on at least the target particle information and predicted dispersion medium information, which is physical property information of a predicted dispersion medium that is a dispersion medium to be predicted; an aggregation diameter prediction step of calculating an aggregation diameter in a particle dispersion obtained by mixing the prediction target particles in the prediction dispersion medium by applying the micro-physical properties calculated in the second micro-physical property calculation step to the dispersibility prediction model; a prediction information output step of outputting the aggregate diameter calculated in the aggregate diameter prediction step as dispersibility prediction information; The dispersion prediction program according to claim 9, further comprising:

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