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Representing potential energy surfaces with neural networks

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Neural networks can efficiently calculate atomistic potential energy surfaces, allowing for large-scale molecular dynamics simulations.

In this talk I’ll describe how so-called high-dimensional neural network potentials, as proposed by Behler and Parrinello [1], can be constructed and fitted to reproduce ab-initio or density functional theory results. I’ll also demonstrate some recent applications of this approach to nuclear quantum effects in electrolyte solutions [2] and anisotropic proton diffusion at solid-liquid interfaces [3].

Moreover, I’ll briefly describe another type of neural network potential based on a graph convolutions, with a particular emphasis on “PiNet”, which we recently demonstrated could predict a wide range of properties for molecules, liquids, and materials [4].

J. Behler, M. Parrinello. Phys. Rev. Lett. 98, 146401
M. Hellstrom, M. Ceriotti, J. Behler. J. Phys. Chem. B 2018, 122, 44, 10158–10171
M. Hellstrom, V. Quaranta, J. Behler. Chem. Sci., 2019, 10, 1232-1243
Y. Shao, M. Hellstrom, P.D. Mitev, L. Knijff, C. Zhang. J. Chem. Inf. Model. 2020, 60, 3, 1184–1193

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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