Abstract
This direct dynamics simulation is widely used in quantitative structure-activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, property prediction, etc. This paper explores the idea of integrating long short-term memory (LSTM) with the trajectory computing of direct dynamics simulations to enhance the performance of the simulation and to improve its usability for research and education. The idea is successfully used to predict the location, energy, and Hessian of atoms in a CO2 reaction system. The results demonstrate that the artificial neural network-based memory model successfully learns the desired features associated with the atomic trajectory and rapidly generates predictions that are in excellent agreement with the results from chemistry dynamics simulations. The accuracy of the prediction is better than expected.