Neural Networks Accelerate the Ab Initio Prediction of Solid-Solid Phase Transitions at High Pressures.

High-level ab initio chemical calculations, such as second-order Møller-Plesset perturbation (MP2), are highly accurate but time-consuming, making it inefficient to apply to macromolecular systems. Here, we propose a newly efficient approach based on the neural network and fragment method to predict the Gibbs free energy, structural characteristics, and thus phase transition of solid crystal structures. The proposed approach has the same prediction accuracy as the MP2 calculation but is hundreds of times faster than the MP2. The predicted structures and phase transitions of two selected ice phases (IX and XV) under extreme conditions are in excellent agreement with the MP2 calculations and experimental results but with an extremely low computational cost. It not only predicts the high-pressure structures and phase diagrams of solid systems accurately and efficiently but also solves the problem of extreme calculation cost during a high-precision theoretical study on high-pressure molecular crystals with potentially essential applications.