QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
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DOI：
10.48550/arxiv.2306.09549
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Haiyang Yu;Meng Liu;Youzhi Luo;A. Strasser;X. Qian;Xiaoning Qian;Shuiwang Ji
中科院分区：
文献类型：
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作者：
Haiyang Yu;Meng Liu;Youzhi Luo;A. Strasser;X. Qian;Xiaoning Qian;Shuiwang Ji
监督式机器学习方法作为第一性原理计算方法（如密度泛函理论（DFT））的替代方法，在加速电子结构预测方面的应用日益增多。虽然众多量子化学数据集侧重于化学性质和原子力，但人们非常期望能够准确且高效地预测哈密顿矩阵，因为它是决定物理系统量子态和化学性质的最重要、最基本的物理量。在这项工作中，我们基于QM9数据集生成了一个新的量子哈密顿量数据集，名为QH9，它为999或2998个分子动力学轨迹以及130831个稳定分子几何结构提供精确的哈密顿矩阵。通过设计针对各种分子的基准任务，我们表明当前的机器学习模型有能力预测任意分子的哈密顿矩阵。QH9数据集和基线模型都通过一个开源基准提供给社区，这对于开发机器学习方法以及加速用于科学和技术应用的分子和材料设计具有很高的价值。我们的基准可在https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench公开获取。
Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 or 2998 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench.