Evidential deep learning for interatomic potentials.

ABSTRACT: Machine learning interatomic potentials have been widely used to facilitate large-scale molecular simulations with accuracy comparable to ab initio methods. To ensure the reliability of the simulation, the training dataset is iteratively expanded through active learning, where uncertainty serves as a critical indicator for identifying and collecting out-of-distribution data. However, existing uncertainty quantification methods tend to involve either expensive computations or compromise prediction accuracy. Here we show an evidential deep learning framework for interatomic potentials with a physics-inspired design. Our method provides uncertainty quantification without significant computational overhead or decreased prediction accuracy, consistently outperforming other methods across a vari

SUBMITTER: Xu H 

PROVIDER: S-EPMC12830583 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature