AI RESEARCH
Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
arXiv CS.LG
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ArXi:2510.03046v2 Announce Type: replace 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