Neural Network Force Field for Molecular Dynamics of Multi-Element Atomistic System

A talk by Jonathan Mailoa
Senior Research Engineer, Computational, Bosch

20 November 2020, 01:00 PM

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About this talk

Neural network-based force field has recently emerged as a way to bypass expensive quantum mechanics calculation in molecular dynamics simulation, which enables us to study material properties and physical mechanisms at the atomistic level. Despite fundamental advances in rotation-invariant symmetry function "fingerprint" data representation, the derivative fingerprints required for the atomic force calculation significantly increases the training and execution runtime required in this approach. In this talk, we present an algorithm to bypass the need for fingerprint.

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