SKM 2023 – wissenschaftliches Programm

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MM: Fachverband Metall- und Materialphysik

MM 36: Data Driven Materials Science: Big Data and Work Flows – Microstructure-Property-Relationships (joint session MM/CPP)

MM 36.5: Vortrag

Donnerstag, 30. März 2023, 11:15–11:30, SCH A 251

Atomic cluster expansion: training a transferable water interatomic potential from the local atomic environments of ice — •Eslam Ibrahim, Yury Lysogorskiy, and Ralf Drautz — ICAMS, Ruhr Universität Bochum, 44780 Bochum, Germany

We show the predictive power of the atomic cluster expansion (ACE) for modeling challenging systems such as water. We trained ACE on data from ab-initio molecular dynamics simulations (AIMD) of water at 300 K. ACE displays excellent agreement to the first-principles reference data in predicting radial distribution functions and covalent and hydrogen bonding characteristics. However, our investigation confirms that describing properties that require sampling a larger fraction of phase space, for example, self-diffusion or the free energies, requires longer AIMD simulation times at different pressures/densities and temperatures. We then show that ice structures provide a more efficient means of sampling the phase space of water: training ACE to diverse ice phases only describes liquid water in quantitative and qualitative agreement with first principles reference data. This reveals a powerful and efficient strategy for building transferable water interatomic potentials without running expensive AIMD.