大数据遇上量子化学近似：Δ-机器学习方法
Big Data meets Quantum Chemistry Approximations: The $Δ$-Machine Learning Approach
AI总结：
本文提出Δ-机器学习方法，将机器学习修正叠加到低成本近似量子化学方法上，以低计算成本实现对大规模分子焓、自由能、熵和电子相关能的高精度预测，并验证了其可迁移性。
摘要：
对所有可能分子的虚拟空间进行化学精度且全面的研究，受到量子化学计算成本的严重限制。我们引入一种复合策略，将机器学习修正添加到计算成本低廉的近似传统量子方法中。经过训练后，可以对焓、自由能、熵和电子相关能进行高精度预测，且所处理的分子集合规模显著大于训练所用集合。对于多达16k个C$_7$H$_{10}$O$_2$构造异构体的热化学性质，我们给出数值证据表明可以达到化学精度。我们还在后Hartree-Fock方法中预测电子相关能，而计算成本仅为Hartree-Fock水平，并建立了分子熵与电子相关之间的定性关系。我们使用在134k个有机分子的1%和10%上训练的半经验量子化学和机器学习模型，证明了该方法的可迁移性，能够以密度泛函理论级别的精度重现其余所有分子的焓。
英文摘要：
Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k constitutional isomers of C$_7$H$_{10}$O$_2$ we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree-Fock methods, at the computational cost of Hartree-Fock, and we establish a qualitative relationship between molecular entropy and electron correlation. The transferability of our approach is demonstrated, using semi-empirical quantum chemistry and machine learning models trained on 1 and 10\% of 134k organic molecules, to reproduce enthalpies of all remaining molecules at density functional theory level of accuracy.
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