Machine learning (ML) plays an important role in quantum chemistry, providing
fast-to-evaluate predictive models for various properties of molecules.
However, as most existing ML models for molecular electronic properties use
density function theory (DFT) databases as the ground truth in training, their
prediction accuracy cannot go beyond the DFT. In this work, we developed a
unified ML method for electronic structures of organic molecules using the
gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon
molecules, our model outperforms the DFT with the widely-used B3LYP functional
in both computation costs and prediction accuracy of various quantum chemical
properties. We apply the model to aromatic compounds and semiconducting
polymers on both ground state and excited state properties, demonstrating its
accuracy and generalization capability to complex systems that are hard to
calculate using CCSD(T)-level methods.

发展了一种统一的机器学习方法，用于有机分子的电子结构，使用 CCSD (T) 计算作为训练数据，模型在计算成本和各种量子化学性质的预测准确度上优于常用的 B3LYP 泛函。将模型应用于芳香化合物和半导体聚合物上，对基态和激发态性质进行了验证，展示了其对于复杂系统的准确性和概括能力。