Learning Inter-Atomic Potentials without Explicit Equivariance

• URL: http://arxiv.org/abs/2510.00027v2
• Date: Wed, 15 Oct 2025 17:55:37 GMT
• Title: Learning Inter-Atomic Potentials without Explicit Equivariance
• Authors: Ahmed A. Elhag, Arun Raja, Alex Morehead, Samuel M. Blau, Garrett M. Morris, Michael M. Bronstein,
• Abstract summary: We introduce TransIP: Transformer-based Inter-Atomic Potentials, a novel training paradigm for interatomic potentials.<n>Our approach guides a generic non-equivariant Transformer-based model to learn SO(3)-equivariance by optimizing its representations in the embedding space.<n>Compared to a data augmentation baseline, TransIP achieves 40% to 60% improvement in performance across varying OMol25 dataset sizes.
• Score: 24.438029202222555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate and scalable machine-learned inter-atomic potentials (MLIPs) are essential for molecular simulations ranging from drug discovery to new material design. Current state-of-the-art models enforce roto-translational symmetries through equivariant neural network architectures, a hard-wired inductive bias that can often lead to reduced flexibility, computational efficiency, and scalability. In this work, we introduce TransIP: Transformer-based Inter-Atomic Potentials, a novel training paradigm for interatomic potentials achieving symmetry compliance without explicit architectural constraints. Our approach guides a generic non-equivariant Transformer-based model to learn SO(3)-equivariance by optimizing its representations in the embedding space. Trained on the recent Open Molecules (OMol25) collection, a large and diverse molecular dataset built specifically for MLIPs and covering different types of molecules (including small organics, biomolecular fragments, and electrolyte-like species), TransIP effectively learns symmetry in its latent space, providing low equivariance error. Further, compared to a data augmentation baseline, TransIP achieves 40% to 60% improvement in performance across varying OMol25 dataset sizes. More broadly, our work shows that learned equivariance can be a powerful and efficient alternative to augmentation-based MLIP models.

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