Neural Network Force Field for Molecular Dynamics of Multi-Element Atomistic System
Neural network-based force field has recently emerged as a way to bypass expensive quantum mechanics calculation in molecular dynamics simulation, which enables us to study material properties and physical mechanisms at the atomistic level. Despite fundamental advances in rotation-invariant symmetry function “fingerprint” data representation, the derivative fingerprints required for the atomic force calculation significantly increases the training and execution runtime required in this approach. In this talk, we present an algorithm to bypass the need for fingerprint derivatives and perform direct atomic force prediction which significantly reduces the computation efforts required for training and executing the neural network force field for molecular dynamics simulations.
Jonathan Mailoa is currently a research engineer at Bosch Research and Technology Center, where he works on atomistic computational material science simulation of materials relevant for energy applications such as batteries and fuel cells. Prior to that, he completed his PhD in Electrical Engineering and Computer Science at MIT, developing novel tandem solar cell device architectures. He is currently interested in developing molecular dynamics force field based on machine learning methods.