A machine learned potential for investigating single crystal to single crystal transformations in complex organic molecular systems.

ABSTRACT: The packing of organic molecular crystals is often dominated by weak non-covalent interactions, making their in situ rearrangement under external stimuli challenging to understand. We investigate a pressure-induced single-crystal-to-single-crystal (SCSC) transformation between two polymorphs of 2,4,5-triiodo-1H-imidazole using machine learning potentials. This process involves the rearrangement of halogen and hydrogen bonds combined with proton transfer within a complex solid-state system. We developed a strategy to progressively approach the transition state along the phase transition path from both ends by using both the α and β crystal phases as initial structures for active learning. This method allowed us to develop a DFT-based machine learning potential that faithfully

SUBMITTER: Zhao C 

PROVIDER: S-EPMC11705379 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature