Machine learning‐based atom contribution method for the prediction of surface charge density profiles and solvent design

Abstract Solvents are widely used in chemical processes. The use of efficient model‐based solvent selection techniques is an option worth considering for rapid identification of candidates with better economic, environment and human health properties. In this paper, an optimization‐based MLAC‐CAMD framework is established for solvent design, where a novel machine learning‐based atom contribution method is developed to predict molecular surface charge density profiles ( σ ‐profiles). In this method, weighted atom‐centered symmetry functions are associated with atomic σ ‐profiles using a high‐dimensional neural network model, successfully leading to a higher prediction accuracy in molecular σ ‐profiles and better isomer identifications compared with group contribution methods. The new method is integrated with the computer‐aided molecular design technique by formulating and solving a mixed‐integer nonlinear programming model, where model complexities are managed with a decomposition‐based strategy. Finally, two case studies involving crystallization and reaction are presented to highlight the wide applicability and effectiveness of the MLAC‐CAMD framework.