Machine learning accelerates quantum mechanics predictions of molecular crystals

Quantum mechanics (QM) approaches (DFT, MP2, CCSD(T), etc.) play an important role in calculating molecules and crystals with a high accuracy and acceptable efficiency. In recent years, with the development of artificial intelligence technology, machine learning (ML) has played an increasingly essential role in accelerating the QM calculations and predictions of molecular crystals, as well as the discovery of novel materials. This review provides state-of-the-art information and prospects for QM theories, fragment-based methods and ML methods, as well as their up-to-date applications in predicting small inorganic molecules, large drug molecules and relevant molecular crystals. The discussed applications include ML potential energy surface (PES) construction, crystal structure prediction (CSP), chemical reaction prediction and predictions of a series of properties, such as structure, energy, atomic force, bond length, chemical shift, superconductivity, super-hardness, vibrational spectra, phase transition and diagram. This work also reviews software and packages built recently based on ML methods for property predictions and PES constructions in the field of physics and chemistry. For the three discussed methods, the most time-consuming one is the high-level all-atom QM method, which is capable of describing electronic structures with high accuracy and thus predicts properties that are consistent with the experimental results. The second one, fragment-based QM method, requires less computational time than all-atom QM, which can accelerate all-atom QM calculations for large systems by dividing the entire system into subsystems, presenting a considerable efficiency increase. The computational complexities for fragment-based QM and all-atom QM are N - N2 and N5-N7 (N is the size of the system), respectively. A well-trained ML model can make the above predictions within seconds while ensuring a high prediction accuracy, where its prediction cost and accuracy are determined by the training data and the training process. Therefore, it is challenging for ML applications in physics and chemistry to generate highly accurate and powerful ML models while ensuring sufficient datasets. This work not only provides an overview of the recent progress in QM theories, fragment-based methods, ML methods and several ML-based software programs and applications on small inorganic molecules, large drug molecules and relevant crystals, but also shed light on ML methods in accelerating QM prediction, optimization and novel crystal material design.