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MM: Fachverband Metall- und Materialphysik

MM 17: Poster Ib

MM 17.3: Poster

Montag, 18. März 2024, 18:30–20:30, Poster F

From ab-inito to experiments: A Python workflow for constructing neuroevolution potentials — •Eric Lindgren¹, Adam Jackson², Zheyong Fan³, Christian Müller⁴, Jan Swenson¹, Thomas Holm-Rod⁵, and Paul Erhart¹ — ¹Department of Physics, Chalmers University of Technology, Gothenburg, Sweden — ²Centre for Sustainable Chemical Technologies and Department of Chemistry, University of Bath, United Kingdom — ³College of Physical Science and Technology, Bohai University, Jinzhou, People's Republic of China — ⁴Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Gothenburg, Sweden — ⁵ESS Data Management and Software Center, Copenhagen, Denmark

Neuroevolution potentials, NEPs, as implemented in the GPUMD package are a highly accurate and computationally efficient type of machine-learned interaction potentials, enabling large scale MD simulations with system sizes up to millions of atoms with ab-initio level accuracy. Here, we present a Python workflow for constructing and sampling NEPs using the `calorine` package, and how the resulting trajectories can be analysed with the `dynasor` package to predict observables from scattering experiments. We focus on our recent work on crystalline benzene as an example system, but the approach is readily extendable to other systems.

Keywords: machine learning; force fields; potentials; molecular dynamics; neural network