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arXiv 2012.08899physics.chem-ph
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基于分子轨道的机器学习的分析方法

Analytical Gradients for Molecular-Orbital-Based Machine Learning

Sebastian J. R. Lee, Tamara Husch, Feizhi Ding, Thomas F. Miller

摘要:

基于分子轨道的机器学习(MOB-ML)能够以获得分子轨道为代价预测准确的相关能。在这里,我们提出了推导,实施和数值演示的MOB-ML分析核梯度制定在一般的拉格朗日框架,以执行正交性,本地化和布里渊约束的分子轨道。MOB-ML梯度框架对于回归技术是通用的(例如,高斯过程回归或神经网络)和MOB特征设计。我们表明,与ISO 17数据集上的其他ML方法相比,MOB-ML梯度是高度准确的,而与其他ML方法的数十万个分子的能量和梯度相比,MOB-ML梯度仅在数百个分子的能量上进行训练。的MOB-ML梯度也示出,以产生准确的优化结构,在计算成本的梯度评估,是可比的Hartree-Fock理论或混合DFT。

英文摘要:

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.

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