论文标题
基于分子的机器学习的分析梯度
Analytical Gradients for Molecular-Orbital-Based Machine Learning
论文作者
论文摘要
基于分子 - 基于轨道的机器学习（MOB-ML）可以以获得分子轨道的成本预测准确的相关能。在这里，我们介绍了MOB-ML分析核梯度的推导，实施和数值证明，这些核梯度是在一般的Lagrangian框架中提出的，以实施对分子轨道上的正交性，定位和Brillouin约束。关于回归技术（例如高斯过程回归或神经网络）和MOB特征设计，MOB-ML梯度框架是一般的。我们表明，与ISO17数据集上的其他ML方法相比，MOB-ML梯度非常准确，而与数百种分子的能量和梯度相比，其他ML方法的能量和梯度仅在能量上接受培训。 MOB-ML梯度还显示出可提供准确的优化结构，以梯度评估的计算成本与Hartree-Fock理论或混合DFT相当。
Molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. The MOB-ML gradient framework is general with respect to the regression technique (e.g., Gaussian process regression or neural networks) and the MOB feature design. We show that MOB-ML gradients are highly accurate compared to other ML methods on the ISO17 data set while only being trained on energies for hundreds of molecules compared to energies and gradients for hundreds of thousands of molecules for the other ML methods. The MOB-ML gradients are also shown to yield accurate optimized structures, at a computational cost for the gradient evaluation that is comparable to Hartree-Fock theory or hybrid DFT.
