A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship

Abstract

Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemical-based products. The life cycles of chemical products involve the procedures of conceptual product designs, experimental investigations, sustainable manufactures through appropriate chemical processes and waste disposals. During these periods, one of the most important keys is the molecular property prediction models associating molecular structures with product properties. In this paper, a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions, such as activity coefficient, and so forth. The workflow of framework consists of three steps. In the first step, a database is created for collections of basic molecular information; in the second step, quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties (pseudo experimental data), which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third step. The whole framework has been carried out within a molecular property prediction toolbox. Two case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.

Graphical abstract

Keywords

molecular property / quantum mechanics / quantitative structure-property relationship / heat of reaction / solid-liquid phase equilibrium

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Acknowledgements

The authors are grateful for the financial supports of the National Natural Science Foundation of China (Grant Nos. 22078041 and 21808025) and “the Fundamental Research Funds for the Central Universities (Grant No. DUT20JC41)”.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://dx.doi.org/10.1007/s11705-021-2060-z and is accessible for authorized users.

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