Neural Network <i>Ab-initio</i> Molecular Dynamics (NNAIMD) for Water and Covalent Glasses

Machine learning has become powerful tool in modern computational materials science. Among diverse applications, molecular dynamics (MD) simulation based on neural network (NN) has been attracting great attentions. With the highly accurate energy landscape encoded by ab-initio molecular dynamics (AIMD) training dataset, our goal is to develop an efficient and robust neural network ab-initio molecular dynamics (NNAIMD) framework to perform multimillion atom and long-time nano seconds to microsecond simulations that provide unprecedented access to materials processes and properties. We have developed a scalable NNAIMD simulation framework that has been successfully applied to different class of materials to compute their structural, dynamical and dielectric properties. In this talk, I will discuss our recent progress and applications to water and medium range order in covalent glasses systems. Work reported here was carried out in collaboration with N. Baradwaj, S. Fukushima, R. K. Kalia, A. Krishnamoorthy, A. Mishra, A. Nakano, P. Rajak, K. Shimamura, F. Shimojo and P. Vashishta.