Dr. Nongnuch Artrith, Department of Mechanical Engineering, MIT. Simulations of realistic catalyst particles critically depend on the accurate description of the underlying potential energy surface (PES). While first principles methods such as density-functional theory (DFT) can provide very accurate energies and forces, they are computationally too demanding to address many interesting systems. High-dimensional Neural Networks (NN) trained to first-principles data have been shown to provide accurately interpolated PESs that can speed up simulations by many orders of magnitude compared to conventional DFT.
Accurate and Efficient High-Dimensional Neural Network Potentials for Atomistic Simulations
Thursday, May 08, 2014 4:00pm - 5:00pm