  Machine learning interatomic potentials (MLIPs) have been widely used to
facilitate large scale molecular simulations with ab initio level accuracy.
However, MLIP-based molecular simulations frequently encounter the issue of
collapse due to decreased prediction accuracy for out-of-distribution (OOD)
data. To mitigate this issue, it is crucial to enrich the training set with
active learning, where uncertainty estimation serves as an effective method for
identifying and collecting OOD data. Therefore, a feasible method for
uncertainty estimation in MLIPs is desired. The existing methods either require
expensive computations or compromise prediction accuracy. In this work, we
introduce evidential deep learning for interatomic potentials (eIP) with a
physics-inspired design. Our experiments demonstrate that eIP consistently
generates reliable uncertainties without incurring notable additional
computational costs, while the prediction accuracy remains unchanged.
Furthermore, we present an eIP-based active learning workflow, where eIP is
used not only to estimate the uncertainty of molecular data but also to perform
uncertainty-driven dynamics simulations. Our findings show that eIP enables
efficient sampling for a more diverse dataset, thereby advancing the
feasibility of MLIP-based molecular simulations.
