Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning.

Machine learning plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules; however, most existing machine learning models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work we developed a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with several widely used hybrid and double-hybrid functionals in terms of both computational cost and prediction accuracy of various quantum chemical properties. We apply the model to aromatic compounds and semiconducting polymers, evaluating both ground- and excited-state properties. The results demonstrate the model's accuracy and generalization capability to complex systems that cannot be calculated using CCSD(T)-level methods due to scaling.