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BranchIP introduces a novel framework for adaptive tensor product computation in equivariant machine learning interatomic potentials, directly addressing the computational bottlenecks of foundation-scale models. By using learned distillation losses, the method accelerates simulations by up to 2.4x while reducing memory usage and offering interpretability into how chemical complexity dictates computational depth. This advancement is significant for scaling atomistic modeling in complex materials without sacrificing physical fidelity.
BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials
arXiv:2610.02013v1 Announce Type: new Abstract: Equivariant machine learning interatomic potentials (MLIPs) have revolutionized atomistic modeling, but accurate treatment of complex materials and molecular systems demands expensive models. This limits simulation length- and time-scales, with tensor products a key computational bottleneck. The recent emergence of foundation-scale MLIPs further exacerbates this challenge. We present Branch Interatomic Potential (BranchIP), a single-model framework for learned adaptive tensor product computation, trained with a novel distillation loss. In our experiments on two systems of physical interest, a heterogeneous catalysis system and a proton-conducting solid acid electrolyte, BranchIP accelerates MLIPs across model sizes by up to $2.4\times$ while reducing memory usage by up to $2.6\times$. This is achieved while maintaining physical fidelity. Furthermore, the learned adaptive computation provides model interpretability by revealing which interactions demand deeper computation and showing how computational depth relates to chemical complexity and dynamics.
Abstract:Equivariant machine learning interatomic potentials (MLIPs) have revolutionized atomistic modeling, but accurate treatment of complex materials and molecular systems demands expensive models. This limits simulation length- and time-scales, with tensor products a key computational bottleneck. The recent emergence of foundation-scale MLIPs further exacerbates this challenge. We present Branch Interatomic Potential (BranchIP), a single-model framework for learned adaptive tensor product computation, trained with a novel distillation loss. In our experiments on two systems of physical interest, a heterogeneous catalysis system and a proton-conducting solid acid electrolyte, BranchIP accelerates MLIPs across model sizes by up to $2.4\times$ while reducing memory usage by up to $2.6\times$. This is achieved while maintaining physical fidelity. Furthermore, the learned adaptive computation provides model interpretability by revealing which interactions demand deeper computation and showing how computational depth relates to chemical complexity and dynamics.
Machine Learning (cs.LG); Applied Physics (physics.app-ph); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)
arXiv:2610.02013 [cs.LG]
(or arXiv:2610.02013v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.02013 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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