Efficient Training of Neural Network Potentials for Chemical and Enzymatic Reactions by Continual Learning.

ABSTRACT: Machine learning (ML) methods have emerged as an efficient surrogate for high-level electronic structure theory, offering precision and computational efficiency. However, the vast conformational and chemical space remains challenging when constructing a general force field. Training data sets typically cover only a limited region of this space, resulting in poor extrapolation performance. Traditional strategies must address this problem by training models from scratch using old and new data sets. In addition, model transferability is crucial for general force field construction. Existing ML force fields, designed for closed systems with no external environmental potential, exhibit limited transferability to complex condensed phase systems such as enzymatic reactions, resulting in inferior

SUBMITTER: Lei YK 

PROVIDER: S-EPMC11912204 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature