Machine Learning and Quantum Materials Dynamics

Priya Darshan Vashishta, PhD
University of Southern California

Seminar Abstract: Machine learning has become powerful tool in computational sciences. 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 training dataset, our goal is to develop an efficient and robust neural network quantum molecular dynamics (NNQMD) framework to perform multimillion-to-billion atom and long-time nano seconds to micro second simulations that provide unprecedented access to physical and chemical processes and properties. I will discuss applications of Deep Learning and Reinforcement Learning for ultra slow processes.

Research reported here is done in collaboration with Rajiv Kalia, Aiichiro Nakano, Ken-ichi Nomura and post-docs and graduate students in the Collaboratory for Advanced Computing and Simulations at USC.

Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles 90089-0242