论文标题
Orbnet：使用对称化的原子轨道特征进行量子化学的深度学习
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features
论文作者
论文摘要
我们介绍了一种机器学习方法，其中使用适应于对称性的原子轨道特征和图形神经网络结构来预测来自Schrodinger方程的能量解决方案。 \ textsc {orbnet}在学习效率和可传递性方面表现出了胜过现有的方法，以预测密度功能理论结果，同时采用了从半经验电子结构计算获得的低成本特征。用于应用于类似药物的分子数据集的应用，包括QM7B-T，QM9，GDB-13-T，Drugbank和Folmsbee和Hutchison的构象基准数据集，\ textsc {orbnet}可以预测，在计算成本的DFT中，在计算成本的化学精确度中可以预测千层次的精确度。
We introduce a machine learning method in which energy solutions from the Schrodinger equation are predicted using symmetry adapted atomic orbitals features and a graph neural-network architecture. \textsc{OrbNet} is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that are obtained from semi-empirical electronic structure calculations. For applications to datasets of drug-like molecules, including QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of Folmsbee and Hutchison, \textsc{OrbNet} predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced.
