[2510.03046] Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing

Computer Science > Machine Learning arXiv:2510.03046 (cs) [Submitted on 3 Oct 2025 (v1), last revised 8 Apr 2026 (this version, v2)] Title:Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing Authors:Soohaeng Yoo Willow, Tae Hyeon Park, Gi Beom Sim, Sung Wook Moon, Seung Kyu Min, D. ChangMo Yang, Hyun Woo Kim, Juho Lee, Chang Woo Myung View a PDF of the paper titled Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing, by Soohaeng Yoo Willow and 7 other authors View PDF HTML (experimental) Abstract:Machine learning potentials (MLPs) have become essential for large-scale atomistic simulations, enabling ab initio-level accuracy with computational efficiency. However, current MLPs struggle with uncertainty quantification, limiting their reliability for active learning, calibration, and out-of-distribution (OOD) detection. We address these challenges by developing Bayesian E(3) equivariant MLPs with iterative restratification of many-body message passing. Our approach introduces the joint energy-force negative log-likelihood (NLL$_\text{JEF}$) loss function, which explicitly models uncertainty in both energies and interatomic forces, yielding substantially improved accuracy compared to conventional NLL losses. We systematically benchmark multiple Bayesian approaches, including deep ensembles with mean-variance estimation, stochastic weight averaging Gaussian, improve...