2022
DOI: 10.26434/chemrxiv-2022-sq34x
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Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks

Abstract: Designing solvent systems is the key to achieving the facile synthesis and separation of desired products from chemical processes. In this regard, many machine-learning models have been developed to predict the solubilities of given solute-solvent pairs. However, breakthroughs in developing predictive models for solubility are needed, which can be accomplished through a remarkable expansion and integration of experimental and computational solubility databases. To maximize predictive accuracy, these two databa… Show more

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