JP7299615B2 - Material analysis system, material analysis method, and material analysis program - Google Patents

Description

One aspect of the present disclosure relates to a material analysis system, material analysis method, and material analysis program.

Techniques for obtaining the transport properties of materials are known. For example, Patent Literature 1 describes a method of evaluating an electron conduction network by an active material in an electrode. This method correlates an observed image of a cross section containing an active material layer and a current collector with the conductivity evaluation result of each active material present in the cross section to determine the electrically active active material in the electrode. It measures the distribution of

JP 2014-81362 A

In the method described in Patent Document 1, it is necessary to measure the electrical conductivity between the active material and the current collector in order to obtain the electrical conductivity evaluation result, and therefore equipment for the measurement is required. Therefore, a technique for easily obtaining the transport properties of materials is desired.

A material analysis system according to one aspect of the present disclosure includes at least one processor. At least one processor acquires a material image depicting a microstructure of the material, analyzes the material image to identify the position of the observed phase, and determines transport corresponding to the material microstructure based on the phase position. A model is generated, transport characteristics are calculated based on the transport model, and transport characteristics data representing the transport characteristics are output.

In this aspect, a transport model corresponding to the microstructure is generated from a material image representing the microstructure of the material, and the transport characteristics are calculated based on the transport model. Therefore, the transportation characteristics of the material can be easily obtained by preparing the material image.

According to one aspect of the present disclosure, material transport properties can be obtained without the need for special equipment.

It is a figure which shows an example of the hardware constitutions of the computer which comprises the material analysis system which concerns on embodiment. It is a figure showing an example of functional composition of a material analysis system concerning an embodiment. 4 is a flow chart showing an example of the operation of the material analysis system according to the embodiment; It is a figure for demonstrating a transportation model. 4 is a flow chart showing an example of a specific calculation procedure for transportation characteristics; It is a figure which shows an example of the visualized transport characteristic data. It is a figure which expands and shows a part of FIG. It is a figure which shows an example of a structure of the material-analysis program which concerns on embodiment. FIG. 10 is a diagram showing an image of sample A in the example; FIG. 10 is a diagram showing an image regarding sample B1 in the example. FIG. 10 is a diagram showing an image regarding sample B2 in the example.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same or equivalent elements are denoted by the same reference numerals, and overlapping descriptions are omitted.

[System configuration]
A material analysis system 10 according to an embodiment is a computer system that estimates transport characteristics of a material by analyzing an image of the microstructure of the material. A material is a substance that is used in making some product. The material is not limited, and may be, for example, a superconductor material, that is, a superconducting material, a battery material (for example, an electrode material), a thermally conductive material, or a structural material such as reinforced concrete. The microstructure of a material refers to a fine structure that can be recognized with a resolution of a small unit scale such as micrometers or nanometers. An image refers to, for example, an image obtained by a microscope, that is, a microscopic image. The type of microscope is not limited, and may be, for example, an electron microscope, a scanning probe microscope, an X-ray microscope, an ultrasonic microscope, or an optical microscope. In the present disclosure, an image showing the microstructure of a material is also referred to as a "material image".

Transport properties refer to the specific properties of a material regarding the movement of physical quantities through a transport medium. The physical quantity and transport medium are not limited. For example, the physical quantity may be mass, charge, energy, heat, or momentum, and correspondingly the transport medium may be electrons, ions, fluids, or lattice vibrations. Therefore, transport properties can be expressed by various indices such as electrical conductivity, ionic conductivity, and thermal conductivity. The material analysis system 10 is capable of estimating transport properties for arbitrary materials and phenomena as long as the creation and destruction of transport media do not occur.

The material analysis system 10 may be composed of one computer, or may be composed of a plurality of computers. When using a plurality of computers, these computers are connected via a communication network such as the Internet or an intranet to logically construct one material analysis system 10 .

FIG. 1 is a diagram showing an example of the hardware configuration of a computer 100 that constitutes the material analysis system 10. As shown in FIG. The computer 100 includes a processor (e.g., CPU) 101 that executes an operating system, application programs, etc., a main memory unit 102 composed of a ROM and a RAM, an auxiliary memory unit 103 composed of a hard disk, a flash memory, etc. It has a communication control unit 104 configured by a network card or a wireless communication module, an input device 105 such as a keyboard and a mouse, and an output device 106 such as a monitor.

Each functional element of the material analysis system 10 is realized by causing the processor 101 or the main storage unit 102 to load predetermined software (for example, a material analysis program P1 to be described later) and causing the processor 101 to execute the software. . The processor 101 operates the communication control unit 104, the input device 105, or the output device 106 according to the software, and reads and writes data in the main storage unit 102 or the auxiliary storage unit 103. FIG. Data or databases necessary for processing are stored in the main memory unit 102 or the auxiliary memory unit 103 .

FIG. 2 is a diagram showing an example of the functional configuration of the material analysis system 10. As shown in FIG. The material analysis system 10 includes an image analysis section 11, a model generation section 12, and a property calculation section 13 as functional components. The image analysis unit 11 is a functional element that identifies the position of the phase to be observed by analyzing the material image. Observation target refers to a substance of interest for observing transport properties in a microstructure. A phase is a portion that is distinguished by uniformity in chemical composition and physical state. The model generator 12 is a functional element that generates a transport model corresponding to the microstructure of the material based on the position of the phase. A transport model is a model that abstractly expresses a system in which physical quantities are transmitted in a microstructure. That is, the transport model is a model relating to physical quantities corresponding to microstructures. The characteristic calculator 13 is a functional element that calculates the transport characteristic based on the transport model.

[System operation]
The operation of the material analysis system 10 will be described, and the material analysis method according to this embodiment will be described. FIG. 3 is a flow chart showing an example of the operation of the material analysis system 10 as a processing flow S1.

In step S11, the image analysis unit 11 acquires a material image. The acquisition method of the material image is not limited. For example, the image analysis unit 11 may acquire material images input from a microscope. Alternatively, the image analysis unit 11 may access a given memory or database and read the material image from that storage device. Alternatively, the image analysis section 11 may receive a material image transmitted from another computer. Alternatively, the image analysis unit 11 may acquire a material image input by a user's operation. In one example, the material image is a 256-level grayscale image.

In step S12, the image analysis unit 11 analyzes the material image to identify the position of the phase to be observed. The image analysis unit 11 identifies the position of the phase using any image analysis method suitable for the material image. The image analysis method is not limited. As an example, if the material image is a 256-gradation grayscale image, the image analysis unit 11 divides the individual pixels of the material image into several groups using posterization, which is a technique for lowering the gradation of the image. Classification may identify the location of several phases containing the observed object. When the material is composed of a plurality of substances, the image analysis unit 11 may process one of the plurality of substances as an observation target and identify the position of the phase of the observation target.

In one example, the image analysis unit 11 identifies the positions of the phases to be observed and the positions of the phases not to be observed. Alternatively, the image analysis unit 11 may identify the position of the phase to be observed and identify the remaining portion as the phase not to be observed. Examples of phases that are not of interest include, but are not limited to, impurity phases and void phases. By specifying each phase, the image analysis unit 11 can recognize which phase each pixel belongs to. In the case of a superconducting material, in one example, the image analysis unit 11 treats the superconducting phase as a phase to be observed and identifies its position, and also identifies the positions of each of the impurity phase and the void phase. As another example, the image analysis unit 11 may identify the position of the superconducting phase and identify the remaining portion as a non-superconducting phase.

In addition to the position of the phase to be observed, the image analysis unit 11 may specify the crystal orientation for each of the plurality of pixels corresponding to the phase. "Crystal orientation" refers to the orientation of a crystal expressed by Miller indices. A method for specifying the crystal orientation is not limited. For example, the image analysis unit 11 may identify the crystal orientation based on the color of each pixel. In this case, the image analysis unit 11 uses previously prepared data indicating the correspondence between RGB values and crystal orientations. The image analysis unit 11 acquires the RGB values of each pixel from the material image, and refers to the corresponding relationship to identify the crystal orientation (θ, φ, ω) of each pixel. Alternatively, the image analysis unit 11 may specify the crystal orientation directly from Electron Backscatter Diffraction (EBSD) data obtained from an electron microscope.

In step S13, the model generation unit 12 generates a transport model based on the position of the observation target phase. In one example, the model generation unit 12 generates a transport model by setting a closed site corresponding to the position of the phase to be observed and setting an open site corresponding to the position of the phase not to be observed. A closed site means a region through which a physical quantity flows, and an open site means a region where no physical quantity flows. A transport model is therefore a model that indicates the position of each closed site and the position of each open site. The model generation unit 12 sets closed sites and open sites corresponding to each of a plurality of pixels forming the material image. If the material is a superconducting material, the model generator 12 sets a closed site corresponding to the position of the superconducting phase, which is the current path, and the position of the phase other than the superconducting phase (for example, impurity phases and voids). A transport model is generated by setting the open sites corresponding to the positions of the phases. The transportation model representation method is not limited. For example, the model generator 12 may express the transport model using various methods or definitions such as basic equations and physical parameters.

FIG. 4 is a diagram for explaining the transportation model. Example (a) of FIG. 4 is a three-dimensional representation of individual sites matched to an actual microstructure, and example (b) of FIG. 4 is a two-dimensional representation of those sites matched to a material image. It is an expression. For convenience, 3-axis coordinates are also shown in each of examples (a) and (b). FIG. 4 shows an example in which an open site 21, a closed site 22, and a closed site 23 are arranged in this order, and a physical quantity 29 flows from the closed site 22 to the closed site 23 along the y-axis direction.

Considering adjacent sites as bonds, the resistivity of a bond connecting closed sites can be defined as 1, and the resistivity of a bond in contact with an open site can be defined as infinity. The resistivity of the bond connecting the closed sites may be a value other than one. The model generator 12 may include the resistivity of each of the multiple bonds in the transport model. The model generator 12 may include other information in the transport model, for example, the maximum transport amount of the crystal corresponding to the observed phase may be included in the transport model. The model generator 12 may include individual crystallographic orientations corresponding to individual sites in the transport model, in which case the model generator 12 sets resistivities for individual sites that depend on the crystallographic orientation. For example, the model generation unit 12 generates an anisotropic parameter of a material, an anisotropic Ginzburg-Landau equation (ρ _x (θ, φ, ω), ρ _y (θ, φ, ω), ρ _z (θ, φ , ω)) and the resistivity of each site is calculated. Then, the model generator 12 sets the resistivity of the bond connecting the closed sites to the average resistivity of the adjacent sites. The model generation unit 12 reads out various reference data such as bond resistivity, crystal maximum transport amount, anisotropic Ginzburg-Landau equation, etc. from a given storage unit, and also uses the reference data to generate a transport model. be able to.

In step S14, the characteristic calculator 13 calculates the transport characteristic based on the transport model. FIG. 5 is a flow chart showing an example of a specific calculation procedure for transportation characteristics.

In step S141, the property calculation unit 13 defines an array indicating individual pixels forming the material image. This means that a plurality of array elements are set corresponding to a plurality of pixels forming the material image.

In step S142, the characteristic calculation unit 13 sets the information of the transport model in an array. Specifically, the characteristic calculation unit 13 sets binary site information indicating whether the site is a closed site or an open site for each array element. Assuming that the array element corresponding to one pixel Px is Ex, if the position corresponding to the pixel Px is a closed site, the characteristic calculation unit 13 sets a value indicating the closed site to the array element Ex, If the position is an open site, a value indicating the open site is set in the array element Ex. If the transport model includes other information, the property calculator 13 may set at least part of the information in the array, for example, at least one of crystal orientation (θ, φ, ω) and resistivity. can be stored in an array.

In step S143, the characteristic calculation unit 13 sets percolate clusters by performing cluster analysis. A percolate cluster is a collection of closed sites through which a physical quantity may pass. Percolation transition is a phenomenon in which the conduction characteristics of a physical quantity from the input end of the microstructure of a material to the output end located opposite to the input end change (more specifically, the phenomenon in which the conduction characteristics rise dramatically). It says.

In step S144, the characteristic calculator 13 calculates the potential amount at each site based on the percolate cluster. A potential quantity is a physical quantity set based on a given reference position. What the potential quantity specifically indicates depends on the type of physical quantity. For example, when estimating the electrotransport property, the potential corresponds to the amount of potential, so the property calculator 13 calculates the potential at each site in this step. It is assumed that the characteristic calculator 13 gives a given physical quantity or potential quantity (for example, voltage) to the input terminal of the transport model. Then, for each of the plurality of sites, the characteristic calculation unit 13 applies a general basic equation regarding the movement of physical quantities with respect to the entry and exit of physical quantities between the site and one or more sites adjacent to the site, A potential amount at the site is calculated. The underlying equation is not limited and may be selected according to phenomena or physical quantities. For example, when it comes to electricity, one or both of Kirchhoff's law and Ohm's law may be used. Kirchhoff's law states that the sum of physical quantities (eg, current) flowing into one site is equal to the sum of physical quantities (eg, current) flowing out of the site. Examples of other fundamental equations include Fourier's law, Fick's law of diffusion, and the like. The characteristic calculation unit 13 calculates the potential amount at least for each closed site.

In step S145, the characteristic calculator 13 calculates the movement physical quantity at each cross section based on the potential quantity at each site. A cross section refers to a cross section perpendicular to the direction from the input end of the material to the output end. A moving physical quantity is a physical quantity that moves from one cross section to the next cross section.

In step S146, the characteristic calculation unit 13 determines whether or not the movement physical quantity of each cross section has converged. Conservation law holds that the physical quantity flowing through a cross section is the same for each cross section. If the physical quantity is electric current, its conservation law is the electric current conservation law. Convergence of the moving physical amounts of the respective cross sections means that the moving physical amounts of the individual cross sections match or fall within a given error range. If the movement physical quantity does not converge, the process returns to step S144 (NO in step S146), and the characteristic calculator 13 recalculates the potential quantity at each site and the movement physical quantity at each cross section.

If the movement physical quantity converges, the process proceeds to step S147 (YES in step S146). In step S147, the characteristic calculator 13 calculates the conductivity of the physical quantity. This conductivity is also called connectivity. The conductivity is expressed by the ratio of the physical quantity of cross section movement in a system of interest to the physical quantity of cross section movement in a system containing no open sites. Based on the potential amount at each site, the characteristic calculator 13 identifies the site that outputs the physical quantity as the output site. Then, the characteristic calculator 13 obtains, as the conductivity, the ratio of the physical quantity of movement of the cross section of the output end in the target system to the physical quantity of movement of the cross section of the output end in the system containing no open sites. The characteristic calculator 13 may calculate the conductivity corresponding to the three-dimensional shape of the material by substituting the calculated ratio into a given fitting formula.

The characteristic calculator 13 can calculate various transport characteristics based on the potential amount at each site. For example, the property calculation unit 13 can obtain at least one of the distribution of the moving physical quantity in the microstructure and the conductivity as the transport property. If the moving physical quantity is electricity, the property calculation unit 13 calculates at least one of the potential distribution, the current distribution, and the electrical conductivity as the transport property (electric transport property) based on the potential at each site. may

Returning to FIG. 3, in step S15, the property calculator 13 outputs the transport property data. Transport property data is electronic data indicative of transport properties. The method of outputting the transportation characteristic data is not limited. For example, the characteristic calculation unit 13 may output the transport characteristic data on a monitor, store the transport characteristic data in a given storage device such as a memory or database, or transmit the transport characteristic data to another computer. Characteristic data may be transmitted. The method of expressing the transport characteristic data is also not limited. For example, the property calculator 13 may express the transport property data by numbers, symbols, character strings, texts, images (computer graphics), or a combination of any two or more of these methods. Alternatively, the property calculator 13 may express the transport property data by superimposing an image of the transport property data (transport property image) generated by computer graphics on the material image. By superimposing the transport property image on the material image, local or global quality in the microstructure can be easily grasped. For example, it is possible to identify locations in a superconducting material where current is concentrated.

FIG. 6 is a diagram showing an example of visualized transport property data. In this example, the material is magnesium diboride (MgB ₂ ) sintered body, which is an example of a superconducting polycrystalline material. Assuming that the current flows from the left end to the right end of the microstructure, the current value at each site is calculated, and by visualizing the calculation results as a current distribution, an image such as that shown in FIG. 6 is obtained. Although it is difficult to understand in FIG. 6, which is shown in grayscale, the current amount at each site is shown in multi-gradation colors in the color image. Therefore, it is possible to easily confirm a local tendency such as a portion where current concentrates and a global tendency of the microstructure. FIG. 7 is an enlarged view of the area 30 shown in FIG. Portions 41 in this region are regions where electricity does not flow, ie impurity and void phases. Both portion 42 and portion 43 are regions in which electricity flows, ie, the superconducting phase (the phase to be observed). The difference in color between portions 42 and 43 indicates the magnitude of the current value, with the current value at portion 43 being higher than the current value at portion 42 in this example. Since the transport properties are obtained for individual sites, ie individual pixels, the local distribution of the transport properties can be ascertained as shown in FIGS. For example, it becomes possible to easily identify where the current is concentrated.

[program]
A material analysis program P1 for realizing the material analysis system 10 will be described with reference to FIG. FIG. 8 is a diagram showing an example of the configuration of the material analysis program P1 according to the embodiment.

The material analysis program P1 includes a main module P10, an image analysis module P11, a model generation module P12, and a property calculation module P13. The main module P10 is a part that controls material analysis in an integrated manner. The image analysis unit 11, the model generation unit 12, and the characteristic calculation unit 13 are realized by executing the image analysis module P11, the model generation module P12, and the characteristic calculation module P13.

The material analysis program P1 may be provided after being fixedly recorded in a non-temporary recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. Alternatively, the material analysis program P1 may be provided via a communication network as a data signal superimposed on a carrier wave.

[effect]
As described above, the material analysis system according to one aspect of the present disclosure includes at least one processor. At least one processor acquires a material image depicting a microstructure of the material, analyzes the material image to identify the position of the observed phase, and determines transport corresponding to the material microstructure based on the phase position. A model is generated, transport characteristics are calculated based on the transport model, and transport characteristics data representing the transport characteristics are output.

A material analysis method according to one aspect of the present invention is performed by a computer system comprising at least one processor. The material analysis method includes the steps of acquiring a material image showing the microstructure of the material, identifying the position of the phase to be observed by analyzing the material image, and identifying the microstructure of the material based on the position of the phase. generating a corresponding transport model; calculating transport characteristics based on the transport model; and outputting transport characteristic data indicative of the transport characteristics.

A material analysis program according to one aspect of the present invention includes a step of acquiring a material image showing a microstructure of a material, a step of identifying the position of a phase to be observed by analyzing the material image, and a phase position. generate a transport model corresponding to the microstructure of the material based on the transport model; calculate transport properties based on the transport model; and output transport property data representing the transport properties.

In this aspect, a transport model corresponding to the microstructure is generated from a material image representing the microstructure of the material, and the transport characteristics are calculated based on the transport model. Therefore, the transportation characteristics of the material can be easily obtained by preparing the material image. Since detailed calculations can be performed based on material images without the need for actual measurement work to obtain the transport properties of materials, further sophistication and speeding up of material analysis can be expected. Transport property data can be used for a variety of purposes, such as material development, material quality determination, and material defect detection.

In a material analysis system according to another aspect, the transport model may be a model relating to physical quantities corresponding to microstructures. By generating a transport model for physical quantities, the transport properties of materials can be calculated in detail.

In a material analysis system according to another aspect, at least one processor is a closed site, which is a region through which a physical quantity flows, or a region in which a physical quantity does not flow, in each of a plurality of pixels of a material image based on the position of a phase. An open site may be set and a transportation model may be generated indicating the position of each of the one or more closed sites and the one or more open sites. By setting closed sites and open sites corresponding to individual pixels, a precise transport model is generated, so detailed transport properties can be calculated.

In the material analysis system according to another aspect, at least one processor may represent the transport property data by superimposing an image of the transport property data on the material image. Such a representation makes it easy to grasp local or global quality in microstructures.

In a material analysis system according to another aspect, the material may be a superconducting material and the transport properties may be electrotransport properties. In this case, the electrotransport properties of superconducting materials can be easily obtained.

Examples will be specifically described below, but the present disclosure is not limited to them.

A polished cross-section of a magnesium diboride (MgB ₂ ) sintered body introduced in the following references was observed with a scanning electron microscope (SEM), and its microscopic image was acquired as a material image. In this example, the material indicated as Bulk-S in the reference material was treated as sample A, and the material indicated as Bulk-D was treated as samples B1 and B2.

(References)
S. Mizutani et al., Understanding routes for high connectivity in ex situ MgB ₂ by self-sintering, SuperconductorScience and Technology, Volume 27, Issue 4, 044012 (2014).

By analyzing the material image, the phase (superconducting phase) of magnesium diboride, which is the object of observation, was analyzed. Then, the material image was binarized into the superconducting phase and other phases (impurity phase and void phase). A transport model was generated by setting closed sites corresponding to the positions of the superconducting phase and setting open sites corresponding to the impurity and void phases.

In order to calculate transport characteristics, a program corresponding to the characteristics calculator 13 was created using a program language called FORTRAN90. Then, using the program, the current at each site and the effective electrical conductivity of the material were obtained, and the calculation results were output in CSV file format.

FIG. 9 shows a microscopic image of sample A, a visualized transport model, and visualized transport property data. Similar to FIG. 9, the sample B1 is shown in FIG. 10 and the sample B2 is shown in FIG. Images of the transport model show closed sites (superconducting phase) in black and open sites (impurity and void phases) in white. The image of the transport property data shows the current distribution and is actually a color image. In FIGS. 9-11 the current paths are generally shown in light gray. From these images, it can be seen that the path is tortuous due to the influence of insulating components within the microstructure of the material. In an actual color image, the amount of current at each site is shown in multi-gradation colors. In FIGS. 9 to 11, portions with a large amount of current are represented in white, and portions with no or little amount of current are represented in black. is represented by Therefore, it is possible to easily confirm the local tendency such as the part where the current concentrates and the global tendency of the microstructure from the image.

Table 1 shows the measured effective electrical conductivity values and estimated values obtained by the method of the present disclosure for each of samples A, B1, and B2. The estimated value (2D) is the result obtained from the image analysis itself, and is the estimated value in the two-dimensional (2D) space of the image. The estimated value (3D) is a value obtained by substituting the estimated value (2D) into the following fitting equation (1), and is an estimated value corresponding to the three-dimensional shape of the material. This formula (1) is a calculation result of the effective electrical conductivity in a two-dimensional system virtually generated by randomly distributing open sites and closed sites on a computer, and a three-dimensional system generated by a similar method. It is a function obtained by approximating the calculation result of the effective electrical conductivity in the system by the least squares method. In equation (1), K _3D is the estimate (3D) and K _2D is the estimate (2D). Table 1 shows the conductivity as a percentage.
_K3D = _0.7729K2D +0.2271 (1)

As shown in FIGS. 9-11 and Table 1, both local and global trends in microstructure could be identified.

[Modification]
The present invention has been described in detail above based on its embodiments and examples. However, the invention is not limited to the above embodiments and examples. Various modifications are possible for the present invention without departing from the gist thereof.

In the present disclosure, the expression “at least one processor executes the first process, the second process, . . . The concept is shown including the case where the executing subject (that is, the processor) of n processes from process 1 to process n changes in the middle. That is, this expression shows a concept including both the case where all of the n processes are executed by the same processor and the case where the processors are changed according to an arbitrary policy in the n processes.

The processing steps of the method executed by at least one processor are not limited to the examples in the above embodiments. For example, some of the steps (processes) described above may be omitted, or the steps may be performed in a different order. Also, any two or more of the steps described above may be combined, and some of the steps may be modified or deleted. Alternatively, other steps may be performed in addition to the above steps.

When comparing two numerical values within a computer system or within a computer, either of the two criteria "greater than" and "greater than" may be used, and the two criteria "less than" and "less than" may be used. Either of the criteria may be used. Selection of such a criterion does not change the technical significance of the process of comparing two numerical values.

DESCRIPTION OF SYMBOLS 10... Material analysis system 11... Image analysis part 12... Model generation part 13... Property calculation part P1... Material analysis program P10... Main module P11... Image analysis module P12... Model generation module P13... Property calculation module.

Claims (7)

comprising at least one processor,
the at least one processor
Acquire a material image that shows the microstructure of the material,
identifying the position of the phase to be observed by analyzing the material image;
generating a transport model corresponding to the microstructure of the material based on the phase positions;
Based on the transport model , calculate the transport characteristics including the distribution of the moving physical quantity corresponding to the microstructure ,
outputting transport property data indicative of said transport properties;
Material analysis system. wherein the transport model is a model relating to physical quantities corresponding to the microstructure;
The material analysis system according to claim 1. the at least one processor
Based on the position of the phase, in each of the plurality of pixels of the material image, set a closed site that is a region where the physical quantity flows, or an open site that is a region where the physical quantity does not flow;
generating the transport model indicating the position of each of the one or more closed sites and the one or more open sites;
The material analysis system according to claim 1 or 2. wherein the at least one processor represents the transport property data by overlaying an image of the transport property data on the material image;
The material analysis system according to any one of claims 1-3. said material is a superconducting material and said transport property is an electrotransport property;
The material analysis system according to any one of claims 1-4. A material analysis method performed by a computer system comprising at least one processor, comprising:
obtaining a material image that captures the microstructure of the material;
identifying the position of the observed phase by analyzing the material image;
generating a transport model corresponding to the microstructure of the material based on the phase positions;
a step of calculating a transport characteristic including a distribution of movement physical quantities corresponding to the microstructure based on the transport model;
and outputting transport property data indicating the transport property. obtaining a material image that captures the microstructure of the material;
identifying the position of the observed phase by analyzing the material image;
generating a transport model corresponding to the microstructure of the material based on the phase positions;
a step of calculating a transport characteristic including a distribution of movement physical quantities corresponding to the microstructure based on the transport model;
A material analysis program for causing a computer system to execute a step of outputting transport property data indicating the transport property.

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