Designing solvent systems using self-evolving solubility databases and graph neural networks.

ABSTRACT: Designing solvent systems is key to achieving the facile synthesis and separation of desired products from chemical processes, so many machine learning models have been developed to predict solubilities. However, breakthroughs are needed to address deficiencies in the model's predictive accuracy and generalizability; this can be addressed by expanding and integrating experimental and computational solubility databases. To maximize predictive accuracy, these two databases should not be trained separately, and they should not be simply combined without reconciling the discrepancies from different magnitudes of errors and uncertainties. Here, we introduce self-evolving solubility databases and graph neural networks developed through semi-supervised self-training approaches. Solubilities from

SUBMITTER: Kim Y 

PROVIDER: S-EPMC10793204 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature