A method for modeling performance of inorganic solid-state electrolytes using graph theory and machine learning

By using graph theory and machine learning methods, the crystal structure of inorganic solid electrolytes is modeled as a crystal network graph. Multi-layer convolution operations are performed using a crystal graph convolutional neural network, which solves the problem of performance prediction and doping design of inorganic solid electrolyte materials, and achieves efficient and accurate performance prediction and doping scheme design.

CN122224366APending Publication Date: 2026-06-16SHANGHAI XUANYI NEW ENERGY DEV CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI XUANYI NEW ENERGY DEV CO LTD
Filing Date
2026-03-11
Publication Date
2026-06-16

AI Technical Summary

⚠Technical Problem

Existing technologies make it difficult to quickly and accurately predict the performance of inorganic solid electrolyte materials and design doping schemes, resulting in lengthy research and development cycles and high costs, which makes it difficult to meet the needs of rapid industrialization of new materials.

⚗Method used

Using graph theory and machine learning methods, the crystal structure of inorganic solid electrolytes is abstracted into a crystal network graph. Multi-layer convolutional operations are performed using a crystal graph convolutional neural network to aggregate the local chemical environment features of atoms, and multi-dimensional performance indicators are obtained through mapping through fully connected layers.

🎯Benefits of technology

This technology enables high-precision performance prediction of inorganic solid electrolytes, shortens the screening cycle, reduces computational resource consumption, provides a scientific theoretical basis, and offers guidance for doping design and industrial applications.

✦ Generated by Eureka AI based on patent content.

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Abstract

A kind of inorganic solid electrolyte performance modeling method using graph theory and machine learning.The method abstracts atoms in inorganic solid electrolyte material into a crystal network graph composed of nodes and edges, after feature embedding, multi-layer convolution is carried out using crystal graph convolutional neural network, and the local chemical environment feature vector of atom is updated by aggregating neighborhood features;Then, through the global aggregation of the convergence layer, the feature vector representing the entire crystal structure is extracted;Finally, through the mapping of the full connection layer, the multi-dimensional performance indexes such as thermodynamics, mechanics and electrochemistry are output.The present application realizes the prediction of the structure-property relationship between material structure and macroscopic performance by precisely depicting the local environment of atoms, thereby guiding the efficient development of new inorganic solid electrolyte materials.
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