Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials.

ABSTRACT: Recent advances in equivariant graph neural networks (GNNs) have made deep learning amenable to developing fast surrogate models to expensive ab initio quantum mechanics (QM) approaches for molecular potential predictions. However, building accurate and transferable potential models using GNNs remains challenging, as the data are greatly limited by the expensive computational costs and level of theory of QM methods, especially for large and complex molecular systems. In this work, we propose denoise pretraining on nonequilibrium molecular conformations to achieve more accurate and transferable GNN potential predictions. Specifically, atomic coordinates of sampled nonequilibrium conformations are perturbed by random noises, and GNNs are pretrained to denoise the perturbed molecular c

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10413865 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature