Intelligent Molecular Identification Approach to High-Efficiency Solvents for Organosulfide Capture Using the Active Machine Learning Framework

There is increasing interest in the development of intelligent strategies for rational design and/or identification of promising solvent compounds for volatile, environmentally unfriendly compound capture. However, typical computer-aided methods require huge datasets and/or suffer from accumulated prediction bias. Here, we constructed a computational framework by introducing a stepwise screen approach for molecular descriptors and molecular active selection machine learning to modify the adequate chemical space iteratively. This framework identifies the optimal solvent candidates by molecular similarity search and iterative molecular addition to the training dataset. In a virtual screening of 126,068 compounds, 2443 solvent candidates were successfully identified for the capture of methyl mercaptan (MeSH), one of the major organosulfides in fossil gases.