Brown University School of Engineering

Accurate and Efficient High-Dimensional Neural Network Potentials for Atomistic Simulations

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Thursday, May 08, 2014 4:00pm - 5:00pm

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.