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 2,399 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
this https URL

本文介绍了一种基于监督学习方法的量子哈密顿矩阵预测模型，该模型利用 QM9 数据集中的分子动力学轨迹和稳定分子几何结构生成了一个准确的 QH9 量子哈密顿矩阵数据集，并设计了各种不同分子的基准测试任务，该模型可对任意分子预测哈密顿矩阵，并在分子设计和材料设计领域具有广泛的应用前景。