[2602.10419] Equivariant Evidential Deep Learning for Interatomic Potentials

Computer Science > Machine Learning arXiv:2602.10419 (cs) [Submitted on 11 Feb 2026 (v1), last revised 3 Apr 2026 (this version, v2)] Title:Equivariant Evidential Deep Learning for Interatomic Potentials Authors:Zhongyao Wang, Taoyong Cui, Jiawen Zou, Shufei Zhang, Bo Yan, Wanli Ouyang, Weimin Tan, Mao Su View a PDF of the paper titled Equivariant Evidential Deep Learning for Interatomic Potentials, by Zhongyao Wang and 7 other authors View PDF HTML (experimental) Abstract:Uncertainty quantification (UQ) is critical for assessing the reliability of machine learning interatomic potentials (MLIPs) in molecular dynamics (MD) simulations, identifying extrapolation regimes and enabling uncertainty-aware workflows such as active learning for training dataset construction. Existing UQ approaches for MLIPs are often limited by high computational cost or suboptimal performance. Evidential deep learning (EDL) provides a theoretically grounded single-model alternative that determines both aleatoric and epistemic uncertainty in a single forward pass. However, extending evidential formulations from scalar targets to vector-valued quantities such as atomic forces introduces substantial challenges, particularly in maintaining statistical self-consistency under rotational transformations. To address this, we propose \textit{Equivariant Evidential Deep Learning for Interatomic Potentials} ($\text{e}^2$IP), a backbone-agnostic framework that models atomic forces and their uncertainty jointl...