Molecular potential energy surface prediction method based on deep learning

By using a deep learning-based molecular potential surface prediction method, combined with machine learning potential models and the GeomeTRIC algorithm, the problem of insufficient computational efficiency and accuracy of traditional methods in high-throughput screening is solved, enabling rapid and accurate optimization and batch processing of molecular structures.

CN122067657APending Publication Date: 2026-05-19DALIAN UNIV OF TECH
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Patent Information

Application Number
CN202610221586.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-25
Publication Date
2026-05-19

AI Technical Summary

Technical Problem

In drug discovery, materials science, and chemical product design, existing technologies often struggle to balance computational efficiency and prediction accuracy in high-throughput screening. Traditional quantum chemical calculations are time-consuming, and molecular force field methods lack sufficient prediction accuracy.

Method used

A deep learning-based molecular potential surface prediction method, combined with machine learning potential models and the GeomeTRIC algorithm, is employed to achieve rapid optimization and accurate prediction of molecular structures.

Benefits of technology

It achieves efficient and accurate prediction of molecular energy and force matrices, simplifies the high-throughput calculation process, and improves computational efficiency and accuracy.

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Abstract

The invention belongs to the technical field of computational chemistry and artificial intelligence crossing, and provides a molecular potential energy surface prediction method based on deep learning. The method comprises the following steps: inputting a structure file of an unoptimized molecule, and reading and predicting energy and force of a current molecular structure by utilizing a machine learning potential model; and then, judging whether the structure is converged or not according to a prediction result by adopting a GeomeTRIC algorithm, and if not, adjusting coordinates to generate a new structure and performing circular prediction until the structure is converged. According to the method, the quantum chemistry and the deep learning technology are combined, the calculation efficiency can be greatly improved, complex inter-atomic interaction in molecules can be fully captured, and the prediction accuracy is ensured; and meanwhile, batch processing and automatic generation of a quantum chemistry software interface are supported, and the method is particularly suitable for high-throughput molecular structure optimization in the fields of drug discovery, material science and chemical product design.
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