Regensburg 2022 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 34: Data Driven Materials Science: Interatomic Potentials / Reduced Dimensions
MM 34.3: Vortrag
Donnerstag, 8. September 2022, 16:15–16:30, H45
Take Two: Δ-Machine Learning for Molecular Co-Crystals — •Simon Wengert1, 2, Gábor Csányi3, Karsten Reuter1, and Johannes T. Margraf1 — 1Fritz Haber Institut der MPG, Berlin, Germany — 2TU Munich, Germany — 3University of Cambridge, UK
Co-crystals are a highly interesting material class, as varying their components and stoichiometry in principle allows tuning supramolecular assemblies towards desired physical properties. The in silico prediction of co-crystal structures represents a daunting task, however, as they span a vast search space and usually feature large unit-cells. This requires theoretical models that are accurate and fast to evaluate, a combination that can in principle be accomplished by modern machine-learned (ML) potentials trained on first-principles data. Crucially, these ML potentials need to account for the description of long-range interactions, which are essential for the stability and structure of molecular crystals. In this contribution, we present a strategy for developing Δ-ML potentials for co-crystals, which use a physical baseline model to describe long-range interactions. The applicability of this approach is demonstrated for co-crystals of variable composition consisting of an active pharmaceutical ingredient and various co-formers. We find that the Δ-ML approach offers a strong and consistent improvement over the density-functional tight binding baseline. Importantly, this even holds true when extrapolating beyond the scope of the training set, for instance in molecular dynamics simulations at ambient conditions.