Learning Inter-Atomic Potentials without Explicit Equivariance
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911556931944448 |
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| author | Elhag, Ahmed A. Raja, Arun Morehead, Alex Blau, Samuel M. Zhao, Hongtao Tyrchan, Christian Nittinger, Eva Morris, Garrett M. Bronstein, Michael M. |
| author_facet | Elhag, Ahmed A. Raja, Arun Morehead, Alex Blau, Samuel M. Zhao, Hongtao Tyrchan, Christian Nittinger, Eva Morris, Garrett M. Bronstein, Michael M. |
| contents | 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 attains comparable performance in machine-learning force fields versus state-of-the-art equivariant baselines. 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 equivariant or augmentation-based MLIP models. Our code is available at: https://github.com/Ahmed-A-A-Elhag/TransIP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Inter-Atomic Potentials without Explicit Equivariance Elhag, Ahmed A. Raja, Arun Morehead, Alex Blau, Samuel M. Zhao, Hongtao Tyrchan, Christian Nittinger, Eva Morris, Garrett M. Bronstein, Michael M. Machine Learning Artificial Intelligence Biomolecules Quantitative Methods I.2.1; J.3 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 attains comparable performance in machine-learning force fields versus state-of-the-art equivariant baselines. 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 equivariant or augmentation-based MLIP models. Our code is available at: https://github.com/Ahmed-A-A-Elhag/TransIP. |
| title | Learning Inter-Atomic Potentials without Explicit Equivariance |
| topic | Machine Learning Artificial Intelligence Biomolecules Quantitative Methods I.2.1; J.3 |
| url | https://arxiv.org/abs/2510.00027 |