Dresden 2020 – scientific programme
The DPG Spring Meeting in Dresden had to be cancelled! Read more ...
Parts | Days | Selection | Search | Updates | Downloads | Help
CPP: Fachverband Chemische Physik und Polymerphysik
CPP 32: Condensed-matter simulations augmented by advanced statistical methodologies (joint session DY/CPP)
CPP 32.7: Talk
Monday, March 16, 2020, 17:15–17:30, HÜL 186
Machine-learning force fields trained on-the-fly with bayesian inference — Ryosuke Jinnouchi1,2, Jonathan Lahnsteiner1, Ferenc Karsai3, Georg Kresse1, and •Menno Bokdam1 — 1University of Vienna, Vienna, Austria — 2oyota Central R&D Labs, Inc., Aichi, Japan — 3VASP Software GmbH, Vienna, Austria
Realistic finite temperature simulations of matter are a formidable challenge for first principles methods. Long simulation times and large length scales are required, demanding years of compute time. We present an on-the-fly machine learning scheme that generates force fields automatically during molecular dynamics simulations[1]. This opens up the required time and length scales, while retaining the distinctive chemical precision of first-principles methods and minimizing the need for human intervention. The method is widely applicable to multi-element complex systems and implemented in the VASP code. We demonstrate its predictive power on the entropy-driven phase transitions of hybrid perovskites (CH3NH3PbI3), which have never been accurately described in simulations.
[1] R. Jinnouchi et al., Phys. Rev. Lett. 122, 225701 (2019)