|About this Abstract
||MS&T21: Materials Science & Technology
||Accelerating Materials Science with Big Data and Machine Learning
||Molecular Dynamics Simulation Using Lagrangian Neural Networks
||Ravinder Bhattoo, N. M. Anoop Krishnan
|On-Site Speaker (Planned)
The accurate interatomic potential energy functions (PEF) are critical for valid molecular dynamics simulations. The interatomic PEF is developed by parameterizing a functional form using experimental data or DFT (Density Functional Theory) simulation data. Therefore, estimating a functional form is critical in determining the interatomic PEF. Herein, we use a neural network (NN) to define the Lagrangian function of an atomic system as NN is well known as a universal function approximator. Further, we use the Euler-Lagrange equation to determine the acceleration of the atomic system. Finally, we train the NN against the simulation trajectories of unary and binary Lennard Jones (LJ) systems created from traditional molecular dynamics (MD) simulations. The trained NN is then used to do MD simulations and demonstrate the energy conservation.