A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings.

ABSTRACT: Co-crystals are a highly interesting material class as varying their components and stoichiometry in principle allows tuning supramolecular assemblies toward 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 fo

SUBMITTER: Wengert S 

PROVIDER: S-EPMC9281391 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature