Machine Learning for Green Solvents: Assessment, Selection and Substitution 🔥 https://doi.org/10.1002/advs.202516851 A data-driven pipeline is presented for assessing the sustainability of solvents and identifying greener substitutes. Three models are trained and evaluated on the GlaxoSmithKline Solvent Sustainability Guide (GSK SSG) to predict “greenness” metrics: a traditional Gaussian Process Regression (GPR) model, a fine-tuned GPT model (FT GPT), and a GPT model using in-context learning (ICL). It is found that GPR slightly outperforms language-based GPT models and is used to evaluate 10,189 solvents, forming GreenSolventDB–the largest public database of green solvent metrics. These predictions are combined with Hansen solubility parameter-based metrics to identify greener solvents with solubility behavior similar to hazardous solvents. This approach is validated through case studies on benzene and diethyl ether, with predicted alternatives aligning well with known greener substitutes. Building on this success, novel alternatives are proposed for the hazardous solvents listed in the GSK SSG. This framework for quantifying solvent sustainability and identifying greener substitutes is expected to significantly accelerate the discovery and adoption of environmentally-friendly solvents. Download GreenSolventDB: https://github.com/Ramprasad-Group/green_solvents/tree/main 📕 Advanced Science (IF = 14.1) #dataset