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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.15492 |
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| _version_ | 1866911391744524288 |
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| author | Reschützegger, Thiago Aykent, Sarp Perin, Gabriel Jacob Nunes, Bruno Henrique Cipcigan, Flaviu Ferreira, Rodrigo Neumann Barros Steiner, Mathias Thiemann, Fabian L. |
| author_facet | Reschützegger, Thiago Aykent, Sarp Perin, Gabriel Jacob Nunes, Bruno Henrique Cipcigan, Flaviu Ferreira, Rodrigo Neumann Barros Steiner, Mathias Thiemann, Fabian L. |
| contents | Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical spaces at a fraction of the cost of quantum-mechanical calculations. However, the most accurate equivariant architectures rely on Clebsch-Gordan tensor products whose computational cost scales steeply with angular resolution, creating a trade-off between model expressiveness and inference speed that ultimately limits practical applications. Here we introduce Geodite, an equivariant message-passing architecture that replaces tensor products while incorporating physical priors to ensure smooth, well-behaved potential energy surfaces. Trained on the Materials Project trajectories dataset of inorganic crystals, Geodite-MP achieves accuracy competitive with leading methods on benchmarks for materials stability prediction, thermal conductivity, phonon-derived properties, and nanosecond-scale molecular dynamics, while running $3\text{--}5\times$ faster than models performing similarly. By combining predictive accuracy, computational efficiency, and physicality, Geodite enables faster large-scale atomistic simulations and high-throughput screening that would otherwise be computationally prohibitive. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15492 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Equivariant Interatomic Potentials without Tensor Products Reschützegger, Thiago Aykent, Sarp Perin, Gabriel Jacob Nunes, Bruno Henrique Cipcigan, Flaviu Ferreira, Rodrigo Neumann Barros Steiner, Mathias Thiemann, Fabian L. Computational Physics Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical spaces at a fraction of the cost of quantum-mechanical calculations. However, the most accurate equivariant architectures rely on Clebsch-Gordan tensor products whose computational cost scales steeply with angular resolution, creating a trade-off between model expressiveness and inference speed that ultimately limits practical applications. Here we introduce Geodite, an equivariant message-passing architecture that replaces tensor products while incorporating physical priors to ensure smooth, well-behaved potential energy surfaces. Trained on the Materials Project trajectories dataset of inorganic crystals, Geodite-MP achieves accuracy competitive with leading methods on benchmarks for materials stability prediction, thermal conductivity, phonon-derived properties, and nanosecond-scale molecular dynamics, while running $3\text{--}5\times$ faster than models performing similarly. By combining predictive accuracy, computational efficiency, and physicality, Geodite enables faster large-scale atomistic simulations and high-throughput screening that would otherwise be computationally prohibitive. |
| title | Equivariant Interatomic Potentials without Tensor Products |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2601.15492 |