论文标题
基于分子 - 轨道的机器学习，用于使用内核添加高斯过程回归的开放式和多引用系统的机器学习
Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression
论文作者
论文摘要
我们在基于分子的机器学习（MOB-ML）中介绍了一种新颖的机器学习策略，即内核加法过程回归（KA-GPR），以通过引入机器学习策略来了解封闭和开放式系统的通用电子结构理论的总相关能力。 MOB-ML（KA-GPR）的学习效率与最小的Criegee分子的原始MOB-ML方法相同，这是具有多引用特征的封闭壳分子。此外，通过示例结构训练，不同小自由基的预测精度可以达到1 kcal/mol的化学精度。 MOB-ML（KA-GPR）也可以产生H10链（封闭）和水OH OH键离解（开放壳）的准确势能表面。为了探索KA-GPR可以描述的化学系统的广度，我们进一步应用MOB-ML准确预测闭合（QM9，QM7B-T，GDB-13-T）的大型基准数据集和开放式（QMSPIN）分子。
We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML (KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML (KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, GDB-13-T) and open-shell (QMSpin) molecules.
