Generative discovery of safer chemical alternatives using diffusion modeling: A case study in green solvent design for cyclohexane/benzene extractive distillation

Abstract

Over the past century, advancements in chemistry have significantly propelled human innovation, enhancing both industrial and consumer products. However, this rapid progression has resulted in chemical pollution increasingly surpassing planetary boundaries, as production and release rates have outpaced our monitoring capabilities. To catalyze more impactful efforts, this study transitions from traditional chemical assessment to inverse chemical design, introducing a generative graph latent diffusion model aimed at discovering safer alternatives. In a case study on the design of green solvents for cyclohexane/benzene extraction distillation, we constructed a design database encompassing functional, environmental hazards, and process constraints. Virtual screening of previous design dataset revealed distinct trade-off trends between these design requirements. Based on the screening outcomes, an unconstrained generative model was developed, which covered a broader chemical space and demonstrated superior capabilities for structural interpolation and extrapolation. To further optimize molecular generation towards desired properties, a multi-objective latent diffusion method was applied, yielding 19 candidate molecules. Of these, 7 were identified in PubChem as the most viable green solvent candidates, while the remaining 12 as potential novel candidates. Overall, this study effectively designed green solvent candidates for safer and more sustainable industrial production, setting a promising precedent for the development of environmentally friendly alternatives in other areas of chemical research.