A Systematic Review on Intensifications of Artificial Intelligence Assisted Green Solvent Development
Huaqiang Wen, Shihao Nan, Di Wu, Quanhu Sun, Tong Yu, Jun Zhang, Saimeng Jin, Weifeng Shen
Abstract
Solvents are indispensable components of chemical processes, and the application of ecofriendly, safe, and efficient solvents is vital for building green chemical processes. Nowadays, new techniques have been applied in discovering and exploring green solvents, among which artificial intelligence (AI) plays an increasingly important role in predicting their physical and chemical properties. Being able to explore the chemical space of green solvents, AI can also be utilized to screen out expected solvents and inversely design new solvents. This review introduces the application of AI assisted green solvent design, focusing on intensification techniques in the processes of green solvent design and property prediction. First, the various intensification techniques of quantitative structure–property relationships (QSPR) employed in the process of solvent property prediction are summarized, including the optimization and intensification of feature extraction, ensemble learning, uncertainty analysis, and interpretability modeling. After that, the basic principles and latest theoretical advances in the application of inverse molecular design for green solvents are reviewed, including high-throughput screening, computer-aided molecular design (CAMD), and deep generative models. Finally, new ideas are proposed for the improvement of each intensification technique in order to better match the high demands of the particular application in green solvent design.