The energy efficiency and technical difficulties of an industrial chemical process are often determined by the physicochemical properties of materials.1 It is desirable to design the right structure of chemicals to meet the targeted material properties, such as the adsorption or emission spectrum of light. However, the research and development (R&D) of functional chemicals has been based largely on researcherâs experiences and experimental trial-and-error. With the advances in computing power and algorithm, Computer-Aided Molecular Design (CAMD)2, 3 technique can now serve as an auxiliary methodology to improve the efficiency of R&D. There are two elements in CAMD: First is the methodology for the prediction of molecular properties, including thermodynamic models, quantum mechanical calculation (QM), and molecular simulation etc.; The second part of CAMD is the optimization algorithm that could automatically search molecules with given requirements of molecular properties, including genetic algorithm (GA), simulated annealing algorithm (SA) etc.4 We have developed a new molecular data structure (MDS)5 that allows for flexible creation of new chemical structures with GA. In this presentation, we will demonstrate the application of the MDS in three different applications: (1) Finding new organic solvents of specified value of octanol-water partition function (Kow).2, 3 (2) Finding new organic solvents of specified value of LUMO-HOMO the LUMO-HOMO gap. (3) Finding new ionic liquids (ILs) for CO2 capture.6 We show that CAMD could provide new chemicals that have better or comparable performance with well-known/commercialized specialty chemicals.
References
- Sandler, S., Chemical, Biochemical, and Engineering Thermodynamics. 2006.
- Hsu, H. H.; Huang, C. H.; Lin, S. T., Fully Automated Molecular Design with Atomic Resolution for Desired Thermophysical Properties. Industrial & Engineering Chemistry Research 2018, 57, (29), 9683-9692.
- Achenie, L.; Venkatasubramanian, V.; Gani, R., Computer-Aided Molecular Design : Theory and Practice. 1st ed ed.; Elsevier: Netherlands, 2003; Vol. 12.
- Austin, N. D. Tools for Computer-Aided Molecular and Mixture Design. Dissertation, Carnegie Mellon University, 2017.
- Hsu, H.-H.; Huang, C.-H.; Lin, S.-T., New Data Structure for Computational Molecular Design with Atomic or Fragment Resolution. Journal of Chemical Information and Modeling 2019, 59, (9), 3703-3713.
- Wang, J.; Song, Z.; Cheng, H.; Chen, L.; Deng, L.; Qi, Z., Computer-Aided Design of Ionic Liquids as Absorbent for Gas Separation Exemplified by CO2 Capture Cases. ACS Sustain. Chem. Eng. 2018, 6, (9), 12025-12035.
Presenter(s)
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