WO2018025618A1 - Material structure searching method and x-ray structural analysis system used in said method - Google Patents

Abstract

[Problem] To provide a material structure searching method and an X-ray structural analysis device used in said method that make it possible to carry out X-ray structural analysis of a multiscale structure without specialized knowledge. [Solution] An X-ray structural analysis system used in a material structure searching method is at least provided with an X-ray source for emitting X-rays, an X-ray irradiation means for irradiating X-rays from the X-ray source onto a sample to be measured, an X-ray detection means for measuring X-rays that have been diffracted or scattered by the sample to be measured, and a means for generating XRDS information including X-ray image characteristics on the basis of the X-rays that have been scattered or diffracted by the sample to be measured and detected by the X-ray detection means. The X-ray structural analysis system is further provided with a means capable of accessing image, analysis method, and physical property databases and an interrelationship information database including associations between the databases.

Description

Material structure search method and X-ray structure analysis system used therefor

It is considered that the physical properties of devices and materials are manifested not only by the structure of the material, particularly by the micro structure such as the arrangement of atoms and molecules, but also by their aggregate structure.

The present invention relates to a structural analysis of a substance using X-ray diffraction for material development in a short period of time. In order to efficiently search a substance structure for realizing the function and physical properties of a target material, the structural analysis is efficiently performed. The present invention relates to a method for searching a material structure centered on smart structural analysis that can be performed in an automated manner and an X-ray structural analysis system used therefor.

In recent years, in the field of research and development of new materials, synthesis of materials, evaluation of materials, and determination of the next research policy based on it are routinely performed. As one method, structural analysis using X-rays is used. However, it is difficult for an X-ray expert to perform structural analysis based on the results obtained by the apparatus. Therefore, there has been a demand for an X-ray structural analysis system that can perform structural analysis without being an X-ray specialist.

Especially, the synthesis of advanced materials requires advanced knowledge of synthesis, and material researchers are rapidly specializing. Therefore, it is possible to evaluate structural information without special knowledge like an X-ray specialist, and confirm whether the material designed by the user has the structure as designed. The advent of an X-ray structural analysis system capable of satisfying the demand has been strongly demanded.

For example, according to the following Patent Document 1, a twin crystal real space unit cell, a reciprocal space basic lattice, and a reciprocal lattice point group are displayed three-dimensionally by using a single crystal X-ray structure analysis apparatus. There has already been proposed a twinning analyzer characterized in that the success rate of the X-ray structural analysis of a twinned sample can be improved by making it easy to understand the three-dimensional correlation between components.

JP 2007-3394 A

However, the above-described twinning analysis apparatus according to the prior art is mainly intended for a crystal having a twinned structure, and more specifically, based on the obtained crystal orientation matrix, a plurality of twinning crystals. In addition to the real space unit lattice of the component, its reciprocal space basic lattice is obtained, and these can be rotated, enlarged / reduced, and horizontally moved, and at the same time, the reciprocal lattice point group in which X-ray diffraction occurs is determined for each twin component. These are displayed in a three-dimensional manner. Therefore, from the displayed image, knowledge of crystallography is still necessary to easily understand the three-dimensional interrelationship, and it has a large-scale system / complex structure / hierarchy. No mention was made of the structure, or the material mixed with different materials.

Therefore, the present invention has been achieved in view of the problems in the prior art described above, and in particular, has a large-scale system / complex structure / hierarchy even without expertise in X-ray structural analysis. Method of substance structure for performing “smart structure analysis” capable of evaluating the structure by X-ray for materials such as complex structures and materials mixed with different materials (multi-scale structure), and X-rays used therefor The object is to provide a structural analysis system.

In the present invention, in the material research, the measurement material to be analyzed is often a structure that can be predicted from the material that has been subjected to the structural analysis in advance. However, the actually measured X-ray diffraction / scattering pattern In particular, the researcher designed it by enabling the prediction of the structure of the target material and its X-ray diffraction / scattering simulation, for example. The present invention provides a method for searching for a material structure that enables efficient structural evaluation when confirming a material, and an X-ray structure analysis system used therefor.

In order to achieve the above object, according to the present invention, first, for each candidate material to be searched, (b) at least one of reflection, transmission, and scattering of the material obtained by actual measurement or simulation using an image. (B) Determined from analysis or simulation by analytical method, or database of estimated structure data of the material, and (c) Actual characteristics or properties obtained by actual measurement or simulation Data is made into a database by measurement or computer simulation, and (d) the related information that can be associated and stored including the mutual association of the data is made into a database, and using the databases from (i) to (d) above, Image (b), structure (b) close to the material to be searched Any or a combination thereof gender (c), the mutual information distance information (d), the search method of structure of materials and judging or extraction is provided.

In the above-described method for searching a material structure, the correlation information extracted or extracted may be added to a database. Alternatively, (b) data obtained by actual measurement or simulation using an image is measured using X-ray, electron beam, or other irradiation including electromagnetic waves including visible light, and is based on the (b) analysis method. Data obtained by actual measurement or simulation is measured by analysis including X-ray, electron beam, mass analysis, NMR analysis, optical analysis, chemical analysis, biochemical analysis, or according to the above (c) physical properties Data obtained by actual measurement or simulation may be measured by physical properties including electrical, thermal, mechanical, chemical, and biochemical properties. Alternatively, for the group of material variables including the atomic / molecular composition, crystallinity, orientation, texture, and mixing ratio that define the material, the most effective set of material variables that lead to the material to be searched is calculated. May be included. Furthermore, with regard to material candidates to be searched, (b) data relating to at least one of X-ray reflection, transmission, and scattering of materials obtained by actual measurement using images is input, and (b) materials obtained by simulation. Input data on at least one of X-ray reflection, transmission, and scattering, and use the data in (a) and (b) above to find an image, structure, or physical property close to the material to be searched These combinations may be determined or extracted or made into a database.

In addition, according to the present invention, in order to achieve the above object as well, an X-ray structural analysis system used in the above-described method for searching a material structure, which includes at least an X-ray source that generates X-rays, X-ray irradiation means for irradiating a sample to be measured with X-rays from the X-ray source, X-ray detection means for measuring X-rays diffracted or scattered by the sample to be measured, and the X-ray detection means And means for generating XRDS information including the traits of the X-ray image based on the diffraction or scattered X-rays of the sample to be measured detected by the step (b), and (b) to (d) above. There is provided an X-ray structural analysis system characterized in that it has means accessible.

The above X-ray structure analysis system is the X-ray structure analysis system described above, and further, whether or not the structure of the sample obtained by the X-ray diffraction / scattering measurement is expected. May be provided with a structure discriminating means having such a discriminating function, or it is estimated that the measured sample is most likely based on the XRDS information obtained by the X-ray diffraction / scattering phenomenon. You may provide the extraction means which extracts a result. The X-ray structure analysis system described above may further include a database unit for storing data including the traits of the XRDS information, or further, a material structure description language (MSDL) is provided. A simulator may be provided that calculates XRDS information from the expected structure of the sample calculated by the information included. in addition,
In the X-ray structure analysis system described above, the simulator for calculating the XRDS information can calculate a structure other than the expected structure of the sample, and the function can be called up from outside and used. Alternatively, the extraction unit may be configured to be accessible to a database unit for storing analysis results and physical property values other than the X-rays of the sample, or machine learning or / And may be configured with artificial intelligence (AI).

According to the present invention described above, X-rays for large-scale systems, complicated structures, hierarchical structures, and materials mixed with different materials (multi-scale structures) can be obtained without specialized X-ray knowledge. It is possible to provide a material structure search method and an X-ray structure analysis apparatus used therefor that are capable of structural analysis and that are excellent in practical use.

It is a front view which shows the whole structure of the X-ray-structure-analysis apparatus used for the search method of the substance structure which becomes one embodiment of this invention. It is sectional drawing which shows the whole structure of the said X-ray-structure-analysis apparatus. It is a front view which shows the mode at the time of operation of the said X-ray structure analysis apparatus. It is a figure which shows an example of the detailed structure inside the said X-ray structure analyzer. It is a block diagram which shows an example of the detail of the internal electrical internal structure of the said X-ray structure analysis apparatus. It is a figure explaining the multiscale structure made into the observation object by the search method of the substance structure which becomes this invention. It is a figure for demonstrating the relationship between real space, reverse space, and observation space in the search method of this invention. It is a figure for showing an example of the XRDS pattern and space symmetry in the search method of this invention. It is a figure for showing an example of the XRDS pattern and space symmetry in the search method of this invention. It is a figure for showing an example of the XRDS pattern and space symmetry in the search method of this invention. It is a figure for showing an example of the XRDS pattern and space symmetry in the search method of this invention. It is a figure for showing an example of the XRDS pattern and space symmetry in the search method of this invention. It is a figure for showing an example of the XRDS pattern and space symmetry in the search method of this invention. It is a figure for showing an example of the XRDS pattern and space symmetry in the search method of this invention. It is a figure explaining the XRDS image simulation which appears by the difference in a crystal body. It is a figure explaining said XRDS image simulation. It is a figure explaining said XRDS image simulation. It is a figure explaining said XRDS image simulation. It is a figure explaining said XRDS image simulation. It is a figure explaining said XRDS image simulation. It is a figure explaining said XRDS image simulation. It is a figure explaining said XRDS image simulation. It is a figure for showing an example of the XRDS image which appears by the difference in the crystal body in the search method of the present invention. It is a figure which shows the wide angle diffraction image (XRDS image) obtained from the P (3HB) film of different extending | stretching degree. It is a figure which shows the image obtained by performing polar coordinate transformation with respect to the said XRDS image. It is a figure which shows the actually measured XRDS pattern for comparing with a simulation RDS image. It is a figure which shows an example of the simulation RDS image obtained by simulation. It is a figure which shows the result of having simulated the remaining elements which cannot be reproduced in the said FIG. It is a figure explaining the method of reproducing a measurement XRDS image by combining said simulation. It is a figure shown about the outline of the analysis procedure of the measurement sample in the X-ray structure analyzer used for the search method of this invention. It is a figure which shows the hierarchical structure of the crystal | crystallization of a polyethylene spherulite as an example of the said measurement sample. It is a figure for showing an example of structure analysis by the above-mentioned X-ray structure analysis device. It is a figure for showing an example of structure analysis by the above-mentioned X-ray structure analysis device. It is a figure which shows the difference of the conventional method and the material development flow by this invention. The conceptual diagram of the system for estimating about the relationship between structural information and a physical property in cooperation with and related with not only an XRDS database but the database of other physical properties, etc. which become another Example of this invention.

Hereinafter, an X-ray structure analysis apparatus used for a material structure search method according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

First, the attached FIG. 1 shows the overall configuration of an X-ray structure analysis apparatus according to an embodiment of the present invention. As is apparent from the figure, FIG. 1 includes an X-ray cover. The front structure of the whole shape of a X-ray measurement system is shown. FIG. 2 shows a cross-sectional structure of the X-ray apparatus according to the line II-II in FIG. The X-ray measurement system 1 shown here includes a base 4 that stores a cooling device 2 and an X-ray generation power supply unit 3, and an X-ray cover 6 that is placed on the base 4.

The X-ray cover 6 has a casing 7 surrounding the X-ray apparatus 9 and a pair of doors 8 provided on the front surface of the casing 7. Reference numeral 10 indicates a handle used when the door 8 is opened and closed. The casing 7 and the door 8 are formed of, for example, an iron plate having a thickness of about 3.2 mm. The pair of doors 8 can be opened as shown by an arrow A in FIG. 3, and various operations can be performed on the X-ray apparatus 9 in this opened state. As the X-ray apparatus 9, X-ray apparatuses having various structures are conceivable. In the present embodiment, an X-ray diffractometer that uses a polycrystalline powder sample as a measurement target is shown as an example. Other X-ray diffractometers include atoms and molecules, and wide-angle X-ray scatterers and small-angle X-ray scatterers that can observe an average structure such as a crystal lattice. Furthermore, as will be described later, an X-ray microscope, an electron microscope, XRF, Raman spectroscopy, mass spectrometry, and the like may be included separately.

The X-ray apparatus, that is, the X-ray diffractometer 9 has an X-ray tube 11 and a goniometer 12 as shown in FIG. The X-ray tube 11 includes a filament 13, a target (also referred to as an “anti-cathode”) 14 disposed so as to face the filament 13, and a casing 15 for storing them in an airtight manner. The filament 13 is energized by the X-ray generation power supply unit 3 in FIG. 1 to generate heat and emit thermoelectrons. A high voltage is applied between the filament 13 and the target 14 by the X-ray generation power supply unit 3, and the thermoelectrons emitted from the filament 13 are accelerated by the high voltage and collide with the target 14. This collision area forms an X-ray focal point F, and X-rays R0 are generated from the X-ray focal point F and diverge.

The target 14 generally generates heat at a high temperature and needs to be cooled. The cooling device 2 in FIG. 1 performs the cooling process. For example, a cooling liquid such as cooling water is flowed around the target 14. Cooling is performed.

In FIG. 4, the goniometer 12 supports the sample S and rotates around a sample axis ω that passes through the X-ray incident point of the sample S, and a sample disposed around the θ turntable 16. And a 2θ turntable 17 that can rotate about the axis ω. In the present embodiment, the sample S is assumed to be a polycrystalline powder sample as an example. A drive device (not shown) for driving the θ turntable 16 and the 2θ turntable 17 is stored in the base 18 of the goniometer 12.

Driven by these driving devices, the θ-rotation table 16 rotates intermittently or continuously at a predetermined angular velocity, so-called θ rotation. Driven by these driving devices, the 2θ rotating table 17 rotates intermittently or continuously in the same direction as the θ rotation at the angular velocity twice the θ rotation, that is, so-called 2θ rotation. The above drive device can be configured by an arbitrary structure, but can be configured by a power transmission structure including a worm and a worm wheel, for example.

A detector arm 19 extending outward in the radial direction is provided on a part of the outer peripheral surface of the 2θ turntable 17, and a light receiving slit 21 and an X-ray detector 22 are placed on the detector arm 19. The X-ray detector 22 is configured by, for example, a two-dimensional pixel detector. A diverging ray restricting slit 23 is disposed between the X-ray tube 11 and the goniometer 12.

Since the X-ray diffractometer 9 is configured as described above, the sample S rotates θ around the sample axis ω by the θ rotation of the θ turntable 16, and at the same time, the light receiving slit 21 and the X-ray detector 22. Is rotated 2θ around the sample axis ω by the 2θ rotating table 19. While the sample S rotates θ and the X-ray detector 22 rotates 2θ, the X-ray R0 generated from the X-ray focal point F in the X-ray tube 11 and diverging is regulated by the divergence regulating slit 23 toward the sample S. It is done. The incident angle of the X-ray incident on the sample S changes according to the θ rotation of the sample S.

When the Bragg diffraction condition is satisfied between the incident angle of the X-rays incident on the sample S and the crystal lattice plane, a diffracted X-ray R1 is generated from the sample S. The diffracted X-ray R1 is focused at the light receiving slit 21, and then received by the X-ray detector 22 to measure the X-ray intensity. As described above, the intensity of the diffracted X-ray R1 corresponding to the angle of the X-ray detector 22 with respect to the incident X-ray R0, that is, the diffraction angle, is measured, and the crystal structure or the like related to the sample S is determined from this measurement result.

Subsequently, FIG. 5A shows an example of the details of the electrical internal configuration constituting the control unit in the X-ray structure analysis apparatus. Of course, the present invention is not limited to the embodiments described below.

The X-ray diffraction apparatus 100 includes the above-described internal configuration, and includes a measurement apparatus 102 that performs measurement using an appropriate substance as a sample, an input apparatus 103 that includes a keyboard, a mouse, and the like, and an image display as a display unit. Device 104, printer 106 as means for printing and outputting the analysis result, CPU (Central Processing Unit) 107, RAM (Random Access Memory) 108, ROM (Read Only Memory) 109, external storage And a hard disk 111 as a medium. These elements are connected to each other by a bus 112.

The image display device 104 is configured by an image display device such as a CRT display or a liquid crystal display, and displays an image on a screen in accordance with an image signal generated by the image control circuit 113. The image control circuit 113 generates an image signal based on the image data input thereto. Image data input to the image control circuit 113 is formed by the operation of various arithmetic means realized by a computer including the CPU 107, RAM 108, ROM 109, and hard disk 111. As the printer 106, an ink plotter, a dot printer, an inkjet printer, an electrostatic transfer printer, or any other printing apparatus having an arbitrary structure can be used. The hard disk 111 can also be configured by a magneto-optical disk, a semiconductor memory, or other storage medium having an arbitrary structure.

Inside the hard disk 111, analysis application software 116 that controls the overall operation of the X-ray diffraction apparatus 100, measurement application software 117 that controls the operation of measurement processing using the measurement apparatus 102, and an image display device 104 are provided. Stored is display application software 118 that manages the operation of the display processing used. These application software implement predetermined functions after being read from the hard disk 111 and transferred to the RAM 108 as necessary.

The X-ray diffractometer 100 further includes, for example, a database placed in a cloud area for storing various measurement results including measurement data obtained by the measurement apparatus 102 described above. In the example of the figure, as will be described later, an XRDS information database 120 that stores the XRDS image data obtained by the measurement apparatus 102, a microscope image database 130 that stores an actual image obtained by an electron microscope, For example, a measurement result obtained by analysis other than X-rays, such as XRF and Raman rays, and other analysis database 140 storing physical property information are shown. Note that these databases do not necessarily have to be mounted inside the X-ray diffraction apparatus 100, and may be connected to each other via a network 150 or the like, for example.

As a file management method for storing a plurality of measurement data in a data file, a method of storing individual measurement data in an individual file is also conceivable. In the present embodiment, as shown in FIG. In addition, a plurality of measurement data are continuously stored in one data file. Note that the storage area described as “condition” in FIG. 5B is an area for storing various information including apparatus information and measurement conditions when measurement data is obtained.

Such measurement conditions include (1) the name of the substance to be measured, (2) the type of measuring device, (3) the measurement temperature range, (4) the measurement start time, (5) the measurement end time, and (6) the measurement angle. Range, (7) moving speed of the scanning movement system, (8) scanning conditions, (9) type of X-rays incident on the sample, (10) whether or not an attachment such as a sample high temperature apparatus was used, and other various conditions Can be considered.

An XRDS (X-ray Diffraction and Scattering) pattern or image is an X-ray received on a plane which is a two-dimensional space of the X-ray detector 22 constituting the measuring apparatus 102, and is a planar shape constituting the detector. The light is received / accumulated by pixels (for example, a CCD or the like) arranged in the array and the intensity thereof is measured. For example, by detecting the intensity of X-rays received by integration for each pixel of the X-ray detector 22, a pattern or image in a two-dimensional space of r and θ can be obtained.

Subsequently, a multiscale structure that is an observation target of the X-ray structure analysis system of the present invention will be described with reference to FIG. As described above, conventional X-ray diffractometers are mainly intended for atoms, molecules, and twin crystal structures, but the multi-scale structure that is the object of observation by the present invention is not limited to them. In addition, it is a structure (high-order assembly structure) that has a large-scale and complicated structure and a hierarchical structure, such as a crystal lattice, a laminated lamella, and a spherulite. In other words, it is a substance in which the aggregate structure of molecules and atoms exists over a wide range of structural scales (from angstroms to μm). For example, small aggregate structures (crystal structures, etc.) are included in larger aggregate structures. This refers to the structure of substances and materials that have a hierarchical structure. Furthermore, a complicated structure formed by a structure in which different kinds of materials are mixed is included.

As an example of such a multi-scale structure, for example, a rubber matrix is mixed with silica particles or carbon black, a crosslinking agent or the like is added to control the physical property value or performance (for example, a tire), or hetero Electronic components that generate and emit light by carrier conduction via excitons at the interface between various organic molecular assemblies (for example, organic solar cells, organic electronics, etc .: The efficiency of the device depends greatly on the shape of the interface. In addition, it is necessary to control the structure at the nano level), and further, materials of lithium ion batteries and fuel cells that generate electricity by ionic conduction (discharge efficiency and lifetime depend greatly on the structure of the substance interface, Control of the structure at the nano level is necessary).

Conventionally, a wide-angle X-ray scattering device, a small-angle X-ray scattering device, or the like is generally used for such a multi-scale sample.

<< Relationship between real space, inverse space, and observation space >>
Here, referring to FIG. 7, the real space is a three-dimensional space such as (x, y, z), and the material structure in the real space is described as an electron density distribution ρ (x, y, z). .

On the other hand, the inverse space is a three-dimensional space given by the Fourier transform of the electron density distribution ρ (x, y, z) in the real space, and given by the scattering amplitude A (Kx, Ky, Kz). Further, the observed quantity is described by the following expression as the scattering intensity I (Kx, Ky, Kz).

Also, due to the nature of the Fourier transform, in the inverse space, the Fourier transform of the electron density distribution in the real space always has a center of symmetry (that is, | A (Kx, Ky, Kz) | = | A (-Kx, -Ky , -Kz) | This means that the mapping from real space to inverse space is bijective but not bijective (not bijective). However, the inverse space appears in the process of calculation, and more specifically, the scattering intensity, which is a measurement amount in the X-ray diffraction apparatus, is surjective to real space but not injective. That is, the “uniqueness” of the measured quantity in the X-ray diffractometer to the real space information is impaired. Therefore, in the present invention, not only the X-ray analysis information but also other related information (composition, molecular structure, etc.) is interpreted to improve the “uniqueness” to the real space information. .

<< XRDS pattern and spatial symmetry >>
The XRDS pattern or image on the observation space obtained by X-ray diffraction or scattering by the target material with respect to the irradiated X-rays reflects the information of the electron density distribution in the real space of the target material. However, the XRDS pattern is a two-dimensional space of r and θ, and does not directly represent symmetry in the real space of the target material that is a three-dimensional space. Therefore, in general, it is difficult to specify the (spatial) arrangement of atoms and molecules constituting a material only with existing XRDS images, and specialized knowledge of X-ray structural analysis is required.

However, the XRDS pattern has a relationship with the information of the electron density distribution in the real space, that is, as shown below, due to the symmetry of the electron density distribution in the real space of the target material. The XRDS pattern that appears is different.

For example, FIGS. 8 to 14 below show examples of real space structures of typical materials and XRDS images obtained thereby.

<Crystal structure>
In FIG. 8, as an example, a NaCl type crystal structure (FIG. 8A), a CaCl type crystal structure (FIG. 8B), and a ZnS (zincblende) type crystal structure (FIG. 8C). ), ZnS (wurtzite) type crystal structure (FIG. 8D), and NiAs type crystal structure (FIG. 8E), respectively. Note that XRDS images (patterns) obtained by these crystal structures are different from each other though not shown here.

Further, in FIG. 9, as another example, a ReO ₃ (rhenium oxide) type crystal structure which is an AO ₃ type oxide (FIG. 9A), and a CaTiO ₃ (perovskite) type which is an ABO ₃ type oxide. The crystal structure (FIG. 9B) and the MgAl ₂ O ₄ (spinel) type crystal structure (FIG. 9C), which is an AB ₂ O ₄ type oxide, are shown. XRDS images (patterns) obtained by these crystal structures also have different patterns although not shown here.

<A collection of microcrystals>
If the target material is a collection of microcrystals and the collection of microcrystals is disordered in its real space structure, as shown in FIG. 10A, the obtained two-dimensional (2D) -XRDS data (pattern) is It becomes a ring.

On the other hand, when these microcrystals are ordered in the real space structure, as shown in FIG. 10B, the obtained two-dimensional (2D) -XRDS data (pattern) is distributed in a ring ( Orientation: In this example, the vertical direction and the horizontal direction are possible. Further, although not shown here, if there is order, it becomes a spot shape (= same as a single crystal having a three-dimensional object).

<Plate-like crystals>
If the crystals of the target material are stacked in an orderly manner, the obtained two-dimensional (2D) -XRDS data (pattern) will be flat spots on the top and bottom as shown in FIG. As the crystal size decreases, the spot expands. Furthermore, as shown in FIG. 11B, when these plate-like crystals are stacked obliquely, flat spots are inclined (so-called orientation). Note that if there are two types of stacking directions, two flat spots are superimposed, and if there are a plurality of directions, the spots are ring-shaped, which is the same as Debye ring.

FIG. 12A shows an XRDS image obtained by using a material having a structure in which a large number of atoms and molecules are arranged between layers in a stacked structure, and FIG. 12B shows atoms arranged between layers. And XRDS image when the molecule is tilted.

Further, FIG. 13A shows an XRDS image of a material having a real space structure in which a large number of rod-like structures are irregularly combined, and FIG. 13B shows an XRDS image in the case of a crystal. Is shown. FIG. 14 shows an XRDS image obtained from a material having a structure in which molecules are connected in a string or rod shape.

Here, the XRDS image that appears due to the difference in crystal bodies is described below.

<Simulation of XRDS image>
The XRDS image by the crystal can be simulated as follows. As shown in FIG. 15, if the length of each unit cell is a, b, c, and the angle between each unit cell is α, β, γ, each vector a, b, c is expressed as (See FIGS. 15A and 15B). Further, if the transformation matrix A is expressed by Equation 4 and the lattice position vector r is expressed by Equation 5 (see FIGS. 15C and 15D), the reciprocal lattice vector is expressed by Equation 6, and the reciprocal lattice matrix B is expressed by Equation 7. It becomes like this. The reciprocal lattice vector r * based on the orientation directions h, k, and l is expressed by Equation 8 (see FIG. 16), and the preferential alignment axes (orienting surfaces) H, K, and L are expressed by Equation 9.

Next, the orientation of the preferential orientation axis (oriented crystal plane) as shown in Equation 10 is set perpendicular to the Z axis. In this case, the rotation matrix E ⁻¹ is expressed by ^Equation 11. Next, ζ is the spin angle with the HKL axis as the center, τ is the tilt angle from the Z axis, δ is the angle from the X axis of the XY plane (see FIG. 17), and the orientation state is the number of the transformation matrix E 12 Further, the orientation conversion matrix U by the apparatus is represented by three axes as shown in Equation 13, and these axes depend on the apparatus. The final position of each reciprocal lattice point (hkl) is expressed by Equation 14.

Subsequently, coordinate transformation for moving the reciprocal lattice coordinates to the center of the Ewald sphere is performed as shown in Equation 15 (see FIG. 18). The vector S represents the surface of the Ewald sphere as shown in Equation 16. Next, reciprocal lattice points satisfying the diffraction condition are selected as shown in Equation 17. Here, (x ″, y ″, z ″) is the final position of each reciprocal lattice point (hkl), 1 / λ is the radius of the Ewald sphere, and λ is the wavelength of the X-ray. The position of the X-ray signal is calculated as in Expression 18. Here, the detector surface is set to x = L on the yz plane, and the detection position is (p _x , p _y ).

The XRDS image obtained by the simulation described above is expected to vary depending on the scattering conditions as described below.

For example, if the xy plane is on the detector (two-dimensional detector) and the z-axis is perpendicular to this plane, the preferential orientation axes (H, K, L) are on the z-axis. At this time, both τ and δ are zero. Ζ is also zero. In this case, as shown in FIG. 19, the X-rays scattered by the crystal are located in the reciprocal lattice on the (x ″, y ″) plane and in contact with the Ewald sphere (two stars in the figure). X-rays directed to (shown) will be detected as spots on the detector. However, the central X-ray is not shown.

Further, in the same state as described above, when ζ randomly varies in the range of −2.5 to 2.5 degrees, as shown in FIG. 20, the position in contact with the Ewald sphere (indicated by an asterisk in the figure). (Shown) increases (4 stars), so that 4 spots are detected on the detector.

Further, FIG. 21 shows the state when ζ randomly varies in the range of −22.5 to 22.5 degrees, and FIG. 22 shows the state when ζ randomly varies in the range of −45 to 45 degrees. Respectively.

That is, according to the above-described simulation, it is possible to predict not only the XRDS image that appears due to the basic structure of the crystal body, but also the XRDS image that appears due to a difference in the aggregate state of the basic structure (ie, structural features). . Therefore, according to the present invention, not only the XRDS image actually detected on the detector (two-dimensional detector) but also the predicted XRDS image obtained by the above-described simulation is used for designing. It becomes possible to perform evaluation of the obtained substances more quickly.

In the following, an XRDS image that appears due to a difference in the aggregate state (physical properties and structural characteristics) of the basic structure already known by the inventors will be briefly described.
<Needle crystals>
If it is straight up and down, spots (bright spots: see FIG. 23) appear on the left and right.
・ Spot position tilts when tilted.
・ If you have multiple directions, there will be multiple spots.
When the needle-like crystals are arranged randomly, a horizontal line (the Milky Way: see FIG. 23) is obtained.
・ When an order begins to form between needle-shaped crystals, intensity distribution is formed on the horizontal line (the Milky Way).
・ When order begins to form between needle-like crystals, the horizontal line (the Milky Way) becomes a spot.

<Liquid crystal phase>
-There is no order in the molecular orientation → becomes isotropic phase (liquid phase) and circular.
・ Molecular orientation is aligned in one direction → Nematic phase (N phase), circular ring looks like an ellipse.
・ There is no order in the molecular orientation, but there is a one-dimensional periodic structure → Same as a smectic phase (= lamellar phase) acicular crystal.
The molecular orientation and the direction of the one-dimensional periodic structure (layer normal) are the same → there is a liquid-like short-range order in the direction perpendicular to the smectic A phase and the layer normal.
The molecular orientation is tilted from the layer normal of the one-dimensional periodic structure → smectic C phase,
Although there is a liquid-like short-range order in the direction perpendicular to the layer normal, the molecular orientation is tilted.
There is a three-dimensional order in the molecular positional relationship → crystalline phase.

As described above, the XRDS pattern partially reflects the symmetry of the electron density distribution in the real space. Therefore, in the present invention, the relationship between the XRDS pattern in the observation space and the electron density distribution in the real space is used. To increase the “uniqueness” of the interpretation from the XRDS pattern of the target material, and present the electron density distribution or material structure (referred to as a real space model) that can be a candidate, or structural features and structural findings Is possible. That is, according to this, it is possible to collate and analogize data by extracting X-ray image features, to easily specify the real space model of the target material, and to further proceed with the analysis. For the user of the engaged X-ray measuring apparatus, it is possible to easily determine whether or not a material or substance having a target structure is obtained, and the efficiency of material development can be increased.

In the present invention, the characteristic image elements of the above-described XRDS image (image) are hereinafter also referred to as “characters”. The types and attributes of these characters include the following as shown in FIG. 15 as an example.
Spot / bright spot (position, peak brightness, blur, etc.)
-Annulus (position, peak brightness, blur, etc.)
・ The Milky Way (position, peak brightness, blur, etc.)
These “characters” are a set of these elements according to the position of each pixel of the X-ray detector 22 composed of the above-described two-dimensional pixel detection element and the intensity of the X-ray detected there. Is expressed by several variables.

<Other related information>
In the above description, in addition to X-ray analysis information, information including “character” that is an XRDS pattern resulting from symmetry with real space is used to enhance the “uniqueness” of information. In addition, the following information can also be used.
-Analytical information other than X-rays, such as XRF, Raman spectroscopy, mass spectrometry, etc.-Microscopic observation (image) information-Structure and known physical property information and information obtained from material informatics methods (physical property values, etc.)

As a more specific example, for example, according to BiomacromoleculescroVol. 6, No.3, 2005, 6, 1803-1809, in Poly [(R) -3-hydroxybutyrate] (P (3HB)) stretched film It has been reported that the basic structure is changed by different stretching methods, and the obtained XRDS image is also changed with the change of the structure. FIG. 24 shows wide angle diffraction images (XRDS images) obtained from P (3HB) films having different stretching degrees.

In order to extract features of structural information from the XRDS pattern, it is also effective to perform so-called polar coordinate conversion that decomposes the actually measured XRDS pattern I (x, y) into radial and azimuth components. In this case, as shown in FIG. 25, I (2θ, β) is obtained by polar coordinate transformation. Structural information corresponding to the structural scale is obtained in the radial direction (2θ direction), and structural information depending on the orientation distribution of the basic structure is obtained in the azimuth angle direction (β direction).

Note that structural information relating to symmetry of the fundamental period can be obtained by normalizing the radial direction with λ / 2sinθ. Further, the intensity distribution in the same 2θ, Σ _2θ I (2θ, β), can be used as the feature amount of the orientation information. Furthermore, in the case where there is a “Tennokawa-like” structural feature that depends on both the 2θ and β parameters, a structural finding such as a so-called fiber structure is obtained, in which a strong one-dimensional periodicity is considered. By such feature extraction from the XRDS pattern, the XRDS pattern is expressed (parameterized) by the feature parameter, and subsequent pattern matching and database matching can be efficiently executed.

<Measured XRDS image and simulation RDS image>
The structure estimation based on the actually detected XRDS image and the predicted XRDS image obtained by simulation will be described below.

Consider, for example, solving a problem of actually measuring an XRDS pattern and estimating its structure, a so-called inverse problem. At this time, FIG. 26 shows the measured XRDS pattern. An XRDS pattern as a reciprocal lattice distribution obtained by simulation assuming that the structural unit is randomly rotated around the c-axis is obtained as shown in FIG. Comparison between FIG. 26 and FIG. 27 shows that the two elements in the vertical direction of FIG. 26 cannot be reproduced by simulation. Therefore, assuming that the c-axis is inclined within a range of ± 5 °, the remaining elements can be simulated as shown in FIG.

That is, as shown in FIG. 29, the measured XRDS image can be reproduced by combining different simulations. In other words, it can be seen that this XRDS pattern is a structure in which the structural unit is randomly rotated around the c-axis and the c-axis is inclined within a range of ± 5 °.

Inversely, by using simulation, a combination of structural elements corresponding to various structural findings is made into a database, and the inverse problem is solved as a forward problem by searching for a match with the actual XRDS pattern. Is also possible. Of course, it is also possible to estimate the lattice constant or the structural unit by simulation.

As described above, regarding the XRDS image which is a group of reciprocal lattice points of X-ray diffraction obtained by the X-ray structural analysis system, various structural features appearing on the XRDS image as well as spots derived from the real space periodicity of the basic structure. In addition to the basic structure of a substance, the arrangement of its atoms and molecules can be further improved by using the patterns resulting from structural observations (including traits such as the above-mentioned “Milky Way” and small arc-shaped patterns). It is possible to obtain various information including the structure. Then, as described below, these information are separately analyzed for XRF, Raman spectroscopy, mass spectrometry, physical property values, etc. (X-ray, electron beam, mass analysis, NMR analysis, optical analysis, chemical analysis, biochemistry, etc. In addition to the results of analysis (including analysis), structural information and physical property values are associated by using machine learning and artificial intelligence (AI). These pieces of information are stored (archived) in the other analysis database 140 described above, and further, extraction of feature information similar to real space images obtained by observation with an X-ray microscope or an electron microscope and feature matching are performed. By performing the search, it is possible to extract and display the result (XRDS information) estimated to be the maximum likelihood. As a result, users can acquire the molecular structure (real space) of new structures designed by themselves, even for large-scale systems, complex structures, and hierarchical multiscale structures). Even without this, structural analysis can be performed by X-rays.

Subsequently, the outline of the measurement sample analysis procedure (material structure search method) in the above-described X-ray structure analysis apparatus according to the present invention (so-called operation by a task controller, which is shown in FIG. ), And a method of analyzing the structure of the polyethylene spherulite crystal shown in FIG. 31 with an X-ray diffractometer will be described as an example with reference to FIG. . In the following procedure, the analysis application software 116 that constitutes a task controller previously installed in the hard disk 111 by the CPU 107, RAM 108, ROM 109, etc., for example, in the X-ray diffraction apparatus 100 shown in FIG. This is done by executing.

In the X-ray structural analysis apparatus according to the present invention, various basic properties, such as elemental composition, molecular structure, etc., as well as specific attributes such as particle size, orientation, etc. The assumed structure information of the electron density distribution in the real space is input in the MSDL (Material Structure Description Language) material structure description language, and these are input to the XRDS simulator 210 for X-ray irradiation image (information) Calculate The data obtained as a result is input to the feature extraction engine 220 as the XRDS image (information) including the information related to the above-mentioned “character”, and obtained together with the measured data and simulation. It is assumed that the data is also archived (stored) in the XRDS information database 120 that stores the data.

In other words, the XRDS simulator 210 describes the predicted / predicted spatial distribution of atoms / molecules in MSDL or the like (real space structure), and further, the known basic attributes (element composition, molecular structure, etc.) and sample-specific attributes ( Particle size, orientation, etc.) (and physical properties including electrical, thermal, mechanical, chemical, and biochemical properties), and convert real space information (3D) into observation space information (3D) . Note that the uniqueness of the information is lost. Further, for comparison / collation (230) with the XRDS information measured by the X-ray diffractometer, the inverse space 3D information is mapped to 2D or 1D, and the operation of dropping the dimension is performed. This operation further impairs the uniqueness of the information.

In addition, various types of samples are separately analyzed other than X-ray diffraction / scattering measurements such as XRF, Raman spectroscopy, mass spectrometry, physical properties, etc. (X-ray, electron beam, mass analysis, NMR analysis, optical analysis, chemical analysis, The results obtained by informatics techniques (including chemical analysis) and simulations are stored (archived) in the other analysis database 140 described above, and further obtained by observation with an X-ray microscope or an electron microscope. The image is stored (archived) in the microscope image database 130.

On the other hand, XRDS information obtained as a result of actual measurement by the X-ray structural analysis apparatus is input to the feature extraction engine 220 and stored with the XRDS information database 120 including the above-described actual measurement data and data obtained by simulation. A comparison / reference operation is performed between them, and the existing XRDS information that is most similar to the input XRDS information is selected using machine learning and artificial intelligence (AI), and obtained together with the related data.

This feature extraction engine 220 (in FIG. 30, the feature extraction engine 220 is shown in two places for convenience, but has the same function), the uniqueness of information is impaired. From the XRDS information obtained by the XRDS simulator 210 and the XRDS information measured by the X-ray diffractometer, not only the known feature quantity due to the symmetry of the space group but also machine learning or artificial intelligence (AI) is used. To extract features and obtain feature quantities. These feature quantities are also stored in the XRDS information database 120. In the XRDS information database, the database 140 is referred to and information on physical property values and actuality is also associated.

The XRDS image of the actual measurement result is input to the comparison / reference engine 230, where the nearest data is obtained by calculating the information distance between the data while referring to the XRDS information database 120. The selected and feature matching analysis results are input to the following maximum likelihood spatial information estimation engine 240.

More specifically, in the comparison / reference engine 230, the XRDS information obtained by the X-ray diffractometer and the XRDS information according to the dimensionality of the XRDS information obtained by the XRDS simulator 210 are converted into information elements (bin, pixel, voxel, etc.) to obtain information distances such as matching degree (correlation coefficient, etc.) and similarity degree. Also, the feature quantity obtained by the feature extraction engine 220 is referred to in the XRDS information database 120, and similar information of feature quantity is extracted by machine learning or artificial intelligence (AI), and search is performed by feature matching.

The maximum likelihood space information estimation engine 240 receives the XRDS information simulated by the XRDS simulator 210 described above together with the microscope image in the microscope image database 130, and the engine 240 uses these. Then, the simulation is performed with the parameters changed, and the result estimated to be the maximum likelihood in the real space information is extracted. At that time, not only the database 140 but also other methods including the database 130 are used to verify the validity and cross-reference with the behavior of the macro substance (for example, physical property values) It also includes a function to verify the validity by checking the sex.

The maximum likelihood space information estimation engine 240 refers to real space information linked to the XRDS information having the most similar feature amount obtained by the comparison / reference engine 230 (estimated structure), and is near the estimated structure. After obtaining an ensemble by using a search method such as the Monte Carlo method, for example, the comparison / reference engine 230 is used to perform a feature amount matching operation. The real space structure having the highest similarity of the feature quantity obtained here is set as the maximum likelihood structure, and the result is output. The maximum likelihood space information estimation engine 240 can be realized by a machine learning or artificial intelligence construction tool such as TensorFlow or Chainer.

Next, structural analysis that can be performed by the X-ray structural analysis apparatus according to the present invention described in detail above will be described with reference to FIGS. 32 and 33. FIG.

As shown in FIG. 32A described above, the user who is the user of the apparatus, according to the X-ray structural analysis apparatus of the present invention, can use the MSDL material structure description language or the like via the input apparatus. By using it, molecular design (real space) of a new structure designed by oneself is performed. This makes it possible to predict the molecular structure / aggregate structure (real space) of a new structure designed in advance using the XRDS simulator 210 described above. That is, it is possible to easily determine whether or not a new structure designed by the user in advance is appropriate.

After that, the user further performs computer simulation using various databases as shown in FIG. 33A based on the predicted molecular structure / aggregate structure (real space) of the new structure. (By the XRDS simulator 210 in FIG. 16). According to this, it is possible to predict the analysis result including the XRDS pattern that will be obtained by the X-ray structure analysis apparatus.

Furthermore, when the user actually performs the new structure with the X-ray structure analysis apparatus according to the present invention, as shown in FIG. By comparing the XRDS pattern obtained in the above with the XRDS pattern obtained by simulation (right side of the figure), the new structure designed by the user matches the molecular structure and aggregate structure of the actually synthesized structure. It is possible to determine whether or not there is no need for knowledge of X-ray structural analysis. At that time, based on the XRDS pattern actually obtained, the comparison / reference engine 230 or the maximum likelihood spatial information estimation engine 240 extracts feature information similar to features or features by machine learning or artificial intelligence (AI). By performing a search by collation, it is possible to extract and display the result (XRDS information) estimated to be the maximum likelihood. As a result, the user has no special knowledge of the X-ray molecular structure (real space) of the new structure that he designed, even for large-scale systems, complex structures, and hierarchical multi-scale structures). In addition, structural analysis can be performed by X-rays.

In the conventional material development flow, the expected mounting characteristics and physical properties as shown in FIG. 34 (A) were set, promising material compositions and structures were predicted from previous experience, knowledge, and intuition, and materials were synthesized. The structure was determined by X-ray diffraction / scattering measurement for the object, the physical property measurement was performed for the object having the intended structure, and the success or failure of the development was finally determined through mounting evaluation. However, as described above, it is not easy to estimate the real space structure from XRDS data, and it is necessary to have expertise to form a single academic area, and it becomes a bottleneck for material development. It was possible.

In the present invention, as shown in FIG. 34 (B), not only experience and intuition but also material composition is examined based on physical properties / structure prediction using data science techniques such as materials informatics, and material synthesis is performed. I do. By linking the X-ray diffraction / scattering measurement and the database, the material design / development period can be greatly shortened by quickly determining whether or not the target structure is possessed.

At that time, various information including the above-mentioned molecular design and the predicted molecular structure / aggregate structure (real space), and also the result of determination including both cases of coincidence and non-coincidence It is preferable to store it as the XRDS information described above. “Positive information” and “Negative information” are a pair, both of which have the same amount of information, which is to acquire new knowledge and information and increase the amount of information possessed. This is because it is necessary information for machine learning and artificial intelligence (AI) to become “smart”. In addition, as a result of comparison / reference, those that are determined to be “highly homologous” and those that are determined to be “low homology” are also stored in the database in the same manner as machine learning and artificial intelligence (AI). “Get smarter”. In addition, instead of simply returning a result of “low homology”, it is possible to inform that there is also a structural model of “high homology”. In this way, by sequentially updating known knowledge and information, the information amount increases in addition to the information amount possessed so far, and smarter machine learning and artificial intelligence (AI) are realized.

In addition, Fig. 35 shows a material search method centered on a smart structural analysis system linked with a database that correlates information on various databases in order to search for a material having the required physical properties. In order to search for a material having the required physical property value f1, the data elements α, β, γ, etc. of the physical property information database, optical / electron microscope image database, and material structure information database are correlated with each other by the interrelated information database. Further, a model f (α, β, γ,...) Is constructed by machine learning or artificial intelligence (AI). A set of parameters (α, β, γ,...) For realizing the required physical property value f1 is obtained by referring to the XRDS information database 120 and optimizing the function f. Thereby, a candidate for a material having the required physical property value f1 is specified.

Furthermore, the present invention can be used for data science methods such as Materials Informatics, and can be validated by providing data to the methods, and can be mutually utilized.

Although various embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments are described in detail for the entire system in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the configurations described. In addition, it is possible to replace a part of the configuration of a certain embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of a certain embodiment. It will be possible to add, delete, and replace other configurations for some of the example configurations.

Furthermore, each of the above-described configurations, functions, processing units, processing means, etc. may be realized by hardware by designing a part or all of them, for example, with an integrated circuit. Each of the above-described configurations, functions, and the like may be realized by software by interpreting and executing a program that realizes each function by the processor. Information such as programs, tables, and files that realize each function can be stored in a recording device such as a memory, hard disk, SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD. I will.

Industrial application fields

The present invention can be widely used in a material structure search method and an X-ray structure analysis system used therefor.

DESCRIPTION OF SYMBOLS 1 ... X-ray measurement system, 9, 100 ... X-ray diffraction apparatus, 102 ... Measurement apparatus, 103 ... Input device, 104 ... Image display apparatus, 107 ... CPU, 108 ... RAM, 109 ... ROM, 111 ... Hard disk, 116 ... Application software for analysis, 117 ... Application software for measurement, 120 ... XRDS information database, 130 ... Microscope image database, 140 ... Other analysis database, 210 ... XRDS simulator, 220 ... Feature extraction engine, 230 ... Comparison / reference engine, 240 ... Maximum likelihood spatial information estimation engine

Claims (16)

1. For candidate materials to explore,
   (A) Data on at least one of reflection, transmission, and scattering of materials obtained by actual measurement or simulation using images is made into a database,
   (B) Make a database of the data of the structure of the material determined or estimated from the actual measurement or simulation by the analytical method,
   (C) Regarding the characteristics obtained by actual measurement or simulation based on physical properties, data is made into a database by actual measurement or computer simulation,
   (D) Creating a database of correlating information that can be associated with each other, including the association of the above data,
   Using the databases (a) to (d) above, either information (b), structure (b), physical property (c) or a combination thereof close to the material to be searched, and information distance information on each other (D) A method for searching for a substance structure, characterized in that determination or extraction is performed.
2. 2. The method for searching a substance structure according to claim 1, wherein the correlation information extracted or extracted is added to a database.
3. In the method for searching for a material structure according to claim 1, the data obtained by the actual measurement or simulation using the image (b) is measured using X-rays, electron beams, or other irradiation including electromagnetic waves including visible light. A method for searching a material structure, characterized by
4. In the substance structure search method according to claim 1, data obtained by actual measurement or simulation by the (b) analysis method is X-ray, electron beam, mass analysis, NMR analysis, optical analysis, chemical analysis, raw analysis. A method for searching a substance structure, characterized by being measured by analysis including chemical analysis.
5. In the method for searching for a material structure according to claim 1, the data obtained by the actual measurement or simulation based on the physical properties is based on physical properties including electrical, thermal, mechanical, chemical, and biochemical properties. A method for searching a material structure, characterized by being measured.
6. The method for searching a substance structure according to one of claims 1 to 5, further comprising: a material variable group including an atomic / molecular composition, crystallinity, orientation, texture, and mixture ratio that defines the material; A method for searching a substance structure, comprising calculating a most effective set of material variables leading to a material to be searched.
7. An X-ray structure analysis system used in the method for searching a material structure according to one of claims 1 to 6, comprising:
   An X-ray source generating X-rays;
   X-ray irradiation means for irradiating a sample to be measured with X-rays from the X-ray source;
   X-ray detection means for measuring X-rays diffracted or scattered by the sample to be measured;
   Means for generating XRDS information including traits of an X-ray image based on diffraction or scattered X-rays of the sample to be measured detected by the X-ray detection means, and
   An X-ray structure analysis system comprising means capable of accessing the databases (a) to (d).
8. The X-ray structural analysis system according to claim 7, further comprising:
   An X-ray structure analysis system comprising: a structure determining unit having a function of determining whether or not a sample structure obtained by the X-ray diffraction / scattering measurement is an expected structure.
9. The X-ray structural analysis system according to claim 7, further comprising:
   An X-ray structure analysis system comprising an extraction means for extracting a result estimated to be the maximum likelihood of a measured sample based on XRDS information obtained by the X-ray diffraction / scattering phenomenon.
10. 8. The X-ray structure analysis system according to claim 7, further comprising a database unit for storing data including traits of the XRDS information.
11. The X-ray structure analysis system according to claim 7, further comprising a simulator for calculating XRDS information from an expected structure of the sample calculated by information including a material structure description language (MSDL). Characteristic X-ray structural analysis system.
12. 12. The X-ray structure analysis system according to claim 11, wherein the simulator for calculating the XRDS information can also calculate a structure other than an expected structure of the sample, and uses the function by calling from the outside. An X-ray structural analysis system characterized by being capable.
13. 10. The X-ray structure analysis system according to claim 9, wherein the extraction unit is further configured to be accessible to a database unit for storing analysis results other than the X-rays of the sample and physical property values. X-ray structure analysis system characterized by this.
14. 10. The X-ray structure analysis system according to claim 9, wherein the extraction means includes machine learning and / or artificial intelligence (AI).
15. For candidate materials to explore,
    (B) Input data on at least one of X-ray reflection, transmission and scattering of materials obtained by actual measurement using images,
    (B) Input data on at least one of X-ray reflection, transmission and scattering of materials obtained by simulation,
    Using the data of (a) and (b) above, search for a substance structure characterized by determining, extracting, or creating a database of any image, structure, or physical property close to the material to be searched, or a combination thereof. Method.
16. 16. The method for searching a substance structure according to claim 15, wherein the physical property is information including not only a basic structure of the substance but also an arrangement structure of atoms and molecules.

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