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arXiv · 2507.05559

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials

Matthew C. Kuner·Aaron D. Kaplan·Kristin A. Persson·Mark Asta·Daryl C. Chrzan
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Abstract

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.

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BibTeXRIS

Matthew C. Kuner, Aaron D. Kaplan, Kristin A. Persson, Mark Asta, Daryl C. Chrzan. 2025-07-08. MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials. https://arxiv.org/abs/2507.05559

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