Accelerated green material and solvent discovery with chemistry- and physics-guided generative AI

Carbon capture, utilization and storage (CCUS), along with lignocellulosic biomass valorization (e.g., lignin, cellulose), are promising decarbonization strategies for hard-to-abate industries. Green solvents, such as deep eutectic solvents and ionic liquids, enable efficient CO₂ capture and selective lignin extraction, enhancing lignin depolymerization into high-value products. However, current molecular design tools are slow and computationally expensive, limiting green material innovation. This study introduces a novel data-driven framework for green material discovery using generative AI, including transformers, generative adversarial networks, and variational autoencoders. The generation process was guided by rule-based and physics- and chemistry-informed models for automatic labeling, with feedback loops to reduce invalid SMILES strings. The approach achieved 70 % molecular validity and 94 % novelty in generating new solvents for CO₂ capture and lignin applications. Model training averaged under one hour, and molecule generation took only seconds, significantly faster than traditional methods. Ensemble machine learning models assessed the environmental sustainability of candidates, and retrosynthesis analysis identified feasible, green synthesis pathways. This flexible, scalable methodology extends beyond solvent discovery to broader applications in process design and optimization, enabling the rapid generation of novel and cost-effective process configurations.