整合37个量子化学数据集，推动分子动力学机器学习势发展
OpenQDC: Open Quantum Data Commons
- 统一37个量子化学数据集，覆盖4亿多个构型
- 支持SchNet等模型训练，发现现有架构挑战
- 开源工具链助力科研人员快速上手
机器学习原子间势（MLIPs）是分子动力学模拟中力场的有力替代方案，可实现精确快速的能量与力计算。然而，构建MLIP所需的量子力学（QM）数据分散在多个存储库中，阻碍了数据获取与模型开发。我们提出openQDC软件包，将来自250多种量子方法、包含4亿多个构型的37个QM数据集整合为单一可访问资源。所有数据均经过精心预处理和标准化，适用于MLIP训练，涵盖有机化学中广泛存在的元素与相互作用。openQDC提供归一化与集成工具，可通过Python便捷调用。使用SchNet、TorchMD-Net和DimeNet等主流架构的实验揭示了当前方法的局限性，并建立了基准排行榜，以加速算法评测与新方法研发。持续更新的数据集将推动量子化学数据开放，促进协作创新，提升MLIP发展水平，支持其在分子动力学领域的应用。
原文摘要 · Abstract (English)
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.
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