In this paper from Xue’s PhD work, SolvPred, an open-source Python framework for inverse design of solvent mixtures based on Hansen solubility parameters (HSPs) is presented. SolvPred allows users to control the composition of solvent mixtures while setting essential experimental constraints. As a case study, SolvPred was applied to optimize a polyimide condensation as a model reaction (performed by Shaohua), where the hydrogen-bonding HSP term was selectively tuned.

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