Dresden 2014 – scientific programme

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O: Fachverband Oberflächenphysik

O 85: Molecular Simulations

O 85.1: Talk

Thursday, April 3, 2014, 17:45–18:00, WIL B321

Representing Complex Potential Energy Surfaces by Artificial Neural Networks — •Christopher Handley and Jörg Behler — Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, D-44780 Bochum, Germany

Computer simulations of large systems are computationally costly,and in many cases intractable, when using ab initio models. More efficient potentials are typically based on approximations representative of particular atomic interactions, and the fitting of these potentials is not straightforward. Neural Networks (NNs) recently have been shown to provide interatomic potentials that are comparable to the accuracy of quantum mechanical calculations[1,2]. They are flexible enough to fit complex functions,to quantum mechancial training data, accurate energies and forces. Here, we present the first steps towards a more transferable NN based upon electronic structure methods.

[1] C. M. Handley and P. L. A. Poplier, J. Phys. Chem. A,114,3371-3383, (2010).

[2] J. Behler, PCCP, 13, 17901-18232 (2011).