Data-Driven Discovery of Polar Organic Cocrystals: Integration of Machine Learning and Automated Screening.

ABSTRACT: Polar organic cocrystals hold significant promise for various advanced technological applications. However, their relatively low occurrence emphasizes the difficulties in achieving the desired polar packing arrangements, making their discovery complex and challenging. Here, we introduce a data-driven method that combines machine learning (ML) with high-throughput (HT) automation to speed up the discovery of polar organic cocrystals. Using ML techniques, we identified key factors that influence polar cocrystal formation, allowing for targeted selection of molecular candidates. We examined 13 cocrystal combinations with chloranilic acid (CA), screening 20 solvent systems for each, which enabled a highly efficient search across a broad chemical space. HT automation further enhanced the synthe

SUBMITTER: Liu H 

PROVIDER: S-EPMC12703666 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature