Indexed by:会议论文

Date of Publication:2021-06-29

Volume:48

Page Number:1807-1812

Key Words:product design; computer-aided molecular design; quantum mechanics; machine learning; surrogate model

Abstract:Chemical industry is focusing more on higher value-added materials compared to commodity chemicals. Chemical-based product design has now become a key topic in chemical engineering. A few computer-aided chemical product design platforms/tools have been developed to help design various chemical products. In this work, a Quantum mechanics/Machine learning-based Computational property prediction tool (QMaC) is developed for chemical product design, aiming to employ the Quantum Mechanics (QM) and Machine Learning (ML) techniques to better design organic solvents, inorganic materials, fertilizers and pesticides, polymers, catalysts and other chemical products for human needs. A case study is given to demonstrate the validity of the developed product design tool.