Sponsor(s): Chemical Industry and Engineering Society of China; Chemical Industry Press
12 issues per year
Current Issue: Issue 10, 2020
CIESC Journal is supervised by China Association for Science and Technology and sponsored by Chemical Industry and Engineering Society of China, and Chemical Industry Press. The predecessor of the journal is Journal of China Chemical Industry Association launched in 1923 and Chemical Engineering launched in 1934. The journal aims at reflecting the major achievements of fundamental research and application in chemical and engineering and other related fields, as well as new technologies and methods. Its scope covers thermodynamics, separation engineering, process system engineering, bio-engineering and chemical engineering, energy and environmental engineering, material chemical engineering and nanotechnology, and modern chemical technology. The journal is included in CA, JST, Pж(AJ), EI, CSCD.
Director: Zhao Yingli
Research advances in deep learning based quantitative structure–property relationship modeling of solvents
CIESC Journal,2020,Vol 71,No. 10
Quantitative structure–property relationship is an important theoretical basis for the design and development of solvent molecules. The establishment of an accurate and reliable predictive model can effectively solve the problems of limited property database resources, large human and material resources consumption, and dangerousness in the experimental process. With the rapid development of artificial intelligence technology, deep learning has made some breakthroughs in chemical industry. In this context, this work reviews the research theories and methods of classical and intelligent modeling and introduces some advances of deep learning in intelligent modeling on large-scale data. In addition, the advantages and application prospects of deep learning techniques in the prediction of various basic physical properties of organics as well as their potential impacts on environment, health, and safety are elaborated. From the angle of the intelligent development of green solvents, the prospects of theoretical and applied researches on quantitative structure–property relationship based on deep learning are outlined in the development of chemical products and processes.