A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915312923836416 |
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| author | Yan, Keqiang Bohde, Montgomery Kryvenko, Andrii Xiang, Ziyu Zhao, Kaiji Zhu, Siya Kolachina, Saagar Sarıtürk, Doğuhan Xie, Jianwen Arroyave, Raymundo Qian, Xiaoning Qian, Xiaofeng Ji, Shuiwang |
| author_facet | Yan, Keqiang Bohde, Montgomery Kryvenko, Andrii Xiang, Ziyu Zhao, Kaiji Zhu, Siya Kolachina, Saagar Sarıtürk, Doğuhan Xie, Jianwen Arroyave, Raymundo Qian, Xiaoning Qian, Xiaofeng Ji, Shuiwang |
| contents | Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLIPs involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform as well, especially when predicting high-order outputs. In contrast, equivariant models can capture high-order symmetries, but are computationally expensive. In this work, we propose HIENet, a hybrid invariant-equivariant materials interatomic potential model that integrates both invariant and equivariant message passing layers, while provably satisfying key physical constraints. HIENet achieves state-of-the-art performance with considerable computational speedups over prior models. Experimental results on both common benchmarks and downstream materials discovery tasks demonstrate the efficiency and effectiveness of HIENet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05771 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures Yan, Keqiang Bohde, Montgomery Kryvenko, Andrii Xiang, Ziyu Zhao, Kaiji Zhu, Siya Kolachina, Saagar Sarıtürk, Doğuhan Xie, Jianwen Arroyave, Raymundo Qian, Xiaoning Qian, Xiaofeng Ji, Shuiwang Machine Learning Materials Science Computational Physics Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLIPs involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform as well, especially when predicting high-order outputs. In contrast, equivariant models can capture high-order symmetries, but are computationally expensive. In this work, we propose HIENet, a hybrid invariant-equivariant materials interatomic potential model that integrates both invariant and equivariant message passing layers, while provably satisfying key physical constraints. HIENet achieves state-of-the-art performance with considerable computational speedups over prior models. Experimental results on both common benchmarks and downstream materials discovery tasks demonstrate the efficiency and effectiveness of HIENet. |
| title | A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures |
| topic | Machine Learning Materials Science Computational Physics |
| url | https://arxiv.org/abs/2503.05771 |