Reducing the Cost of Neural Network Potential Generation for Reactive Molecular Systems.

ABSTRACT: Although machine learning potentials have recently had a substantial impact on molecular simulations, the construction of a robust training set can still become a limiting factor, especially due to the requirement of a reference ab initio simulation that covers all the relevant geometries of the system. Recognizing that this can be prohibitive for certain systems, we develop the method of transition tube sampling that mitigates the computational cost of training set and model generation. In this approach, we generate classical or quantum thermal geometries around a transition path describing a conformational change or a chemical reaction using only a sparse set of local normal mode expansions along this path and select from these geometries by an active learning protocol. This yields a tra

SUBMITTER: Brezina K 

PROVIDER: S-EPMC10569056 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature