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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.15600 |
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| _version_ | 1866913518130823168 |
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| author | Khorana, Rahul Noack, Marcus Qian, Jin |
| author_facet | Khorana, Rahul Noack, Marcus Qian, Jin |
| contents | Developing robust representations of chemical structures that enable models to learn topological inductive biases is challenging. In this manuscript, we present a representation of atomistic systems. We begin by proving that our representation satisfies all structural, geometric, efficiency, and generalizability constraints. Afterward, we provide a general algorithm to encode any atomistic system. Finally, we report performance comparable to state-of-the-art methods on numerous tasks. We open-source all code and datasets. The code and data are available at https://github.com/rahulkhorana/PolyatomicComplexes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15600 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Polyatomic Complexes: A topologically-informed learning representation for atomistic systems Khorana, Rahul Noack, Marcus Qian, Jin Machine Learning Computational Physics Developing robust representations of chemical structures that enable models to learn topological inductive biases is challenging. In this manuscript, we present a representation of atomistic systems. We begin by proving that our representation satisfies all structural, geometric, efficiency, and generalizability constraints. Afterward, we provide a general algorithm to encode any atomistic system. Finally, we report performance comparable to state-of-the-art methods on numerous tasks. We open-source all code and datasets. The code and data are available at https://github.com/rahulkhorana/PolyatomicComplexes. |
| title | Polyatomic Complexes: A topologically-informed learning representation for atomistic systems |
| topic | Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2409.15600 |