mlsanityData to 13 Oct 2026

OpenQDC: Open Quantum Data Commons

arXiv 2411.19629 ↗First posted 29 Nov 2024physics.chem-ph cs.LGcited by 0

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Abstract (arXiv)

Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and force calculations. However, Quantum-Mechanical (QM) datasets, crucial for MLIPs, are fragmented across various repositories, hindering accessibility and model development. We introduce the openQDC package, consolidating 37 QM datasets from over 250 quantum methods and 400 million geometries into a single, accessible resource. These datasets are meticulously preprocessed, and standardized for MLIP training, covering a wide range of chemical elements and interactions relevant in organic chemistry. OpenQDC includes tools for normalization and integration, easily accessible via Python. Experiments with well-known architectures like SchNet, TorchMD-Net, and DimeNet reveal challenges for those architectures and constitute a leaderboard to accelerate benchmarking and guide novel algorithms development. Continuously adding datasets to OpenQDC will democratize QM dataset access, foster more collaboration and innovation, enhance MLIP development, and support their adoption in the MD field.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 34 of its 109 references that are in the map. First posted on arXiv 29 Nov 2024.

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References · 34

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22 more
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