Computing device, computing method, program, and machine learning model generation method

By using machine learning models, especially neural network models, and utilizing the list of d-values ​​in the distribution diagram of X-ray powder diffraction, the problems of long calculation time and low accuracy of lattice volume and lattice constant in the prior art have been solved, and high-precision lattice volume inference and rapid lattice constant determination have been achieved.

CN122156925APending Publication Date: 2026-06-05RIGAKU CORP
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
RIGAKU CORP
Filing Date
2025-12-01
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing techniques for inferring lattice volume and lattice constant from X-ray powder diffraction patterns suffer from long processing times and the inability to accurately calculate monoclinic or triclinic lattice constants.

Method used

By employing machine learning models, especially neural network models, and utilizing the list of d-values ​​in the distribution diagram of X-ray powder diffraction, combined with crystal system information, the lattice volume is inferred, and the lattice volume is used to help determine the lattice constant, thereby shortening the computation time.

Benefits of technology

It enables high-precision deduction of lattice volume from X-ray powder diffraction patterns and accurate calculation of lattice constants in a short time. It is applicable to various crystal systems, especially monoclinic and triclinic lattices.

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Abstract

Provided are a computing device, a computing method, a program, and a machine learning model generation method for inferring a lattice volume from a distribution pattern of X-ray powder diffraction. A computing device (100) for inferring a lattice volume from a distribution pattern of X-ray powder diffraction includes an information acquisition unit (110) that acquires information related to the distribution pattern of X-ray powder diffraction, and an inference unit (120) that includes a machine learning model that takes information related to the distribution pattern of X-ray powder diffraction as input and outputs an inferred lattice volume, and infers the lattice volume from the information related to the distribution pattern of X-ray powder diffraction acquired by the information acquisition unit (110).
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