Advanced Research in Machine Learning in Chemistry
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".
Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 7227
Special Issue Editor
Interests: electronic structure theory; reaction design; machine learning; reaction mechanism; computational chemistry
Special Issue Information
Dear Colleagues,
The basic challenge in chemistry is to synthesize stable chemical compounds with desirable functionalities among an astronomical number of all the possible combinations of distinct atoms by designing efficient chemical reactions. Machine learning in recent years has emerged as a promising tool to solve some long-standing problems in chemistry. It is now applied to promote theoretical and computational chemistry, discovery of new reactions, new catalysts, and drug molecules, structural characterization, and so on. For example, machine learning has been used to predict properties of molecules and materials from large databases without doing direct first-principles calculations, or developing universal force fields or atomic potentials with qualities of quantum mechanics for general molecules or materials, or more accurate density functionals for density functional theory. Other applications of machine learning include: building more accurate quantitative structure–activity relationships, designing efficient synthetic routes for a target molecule in organic synthesis, and developing highly efficient structural characterization tools based on a combination of X-ray and spectroscopy results, and so on.
This Special Issue is designed to gather scientific papers on applications of machine learning in various subfields of chemistry. Broad applications of machine learning in theoretical and computational modeling, chemical synthesis, structural analysis, and discovery of new compounds or reactions, and other subjects, can be discussed.
Prof. Dr. Shuhua Li
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- neural network
- interatomic potentials
- structure-activity relationship
- density functional
- molecular properties
- chemical synthesis
- structural characterization
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