MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916831951847424 |
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| author | Kuner, Matthew C. Kaplan, Aaron D. Persson, Kristin A. Asta, Mark Chrzan, Daryl C. |
| author_facet | Kuner, Matthew C. Kaplan, Aaron D. Persson, Kristin A. Asta, Mark Chrzan, Daryl C. |
| contents | We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynamic stability under extreme temperatures and pressures. MP-ALOE shows strong performance on all of these benchmarks, and is made public for the broader community to utilize. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials Kuner, Matthew C. Kaplan, Aaron D. Persson, Kristin A. Asta, Mark Chrzan, Daryl C. Materials Science Artificial Intelligence Computational Physics We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynamic stability under extreme temperatures and pressures. MP-ALOE shows strong performance on all of these benchmarks, and is made public for the broader community to utilize. |
| title | MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials |
| topic | Materials Science Artificial Intelligence Computational Physics |
| url | https://arxiv.org/abs/2507.05559 |