Calibrating optical spectra using machine learning algorithms
https://doi.org/10.26907/mrsej-20102
Abstract
We suggest an approach using machine learning random forest algorithms to comparing and calibrating the results of calculations of transition energies in organic molecules by ZINDO/S (Zerner's intermediate neglect of differential overlap) and TDDFT (time-dependent density-functional theory) methods. We show how our machine learning model, trained on a relatively small data set can improve the results of semi-empirical methods and obtain absorption spectra comparable to TDDFT calculations.
Supporting agencies: This work was supported by the subsidy of the Ministry of Science and Higher Education of the Russian Federation (3.2166.2017) allocated to Kazan Federal University for performing the project part of the state assignment in the area of scientific activities.
About the Author
M. A. Shakirov
Kazan Federal University
Russian Federation
Russian Federation
Кремлевская, 18, 420008 Казань
References
1. Cramer C. J. Essentials of computational chemistry: theories and models (John Wiley &Sons, 2013).
2. Lewars E. G. Computational Chemistry: Introduction to the Theory and Applications of Molecular and Quantum Mechanics (Kluwer Academic Publishers, 2003).
3. Grigorenko B. L., Mironov V. A., Polyakov I. V., Nemukhin A. V. Supercomputing Frontiersand Innovations5, 62 (2018).
4. Rodriguez-Galiano V., Sanchez-Castillo M., Chica-Olmo M., Chica-Rivas M. Ore GeologyReviews71, 804 (2015).
5. Ghasemi J. B., Tavakoli H. Analytical Methods5, 1863 (2013).
6. Montavon G., Rupp M., Gobre V., Vazquez-Mayagoitia A., Hansen K., Tkatchenko A., Müller K. -R., von Lilienfeld O. A. New Journal of Physics15, 095003 (2013).
7. Pronobis W., Schütt K. T., Tkatchenko A., Müller K. -R. The European Physical Journal B91, 178 (2018).
8. Dral P. O., von Lilienfeld O. A., Thiel W. Journal of Chemical Theory and Computation11, 2120 (2015).
9. Ramakrishnan R., Hartmann M., Tapavicza E., Von Lilienfeld O. A. The Journal of Chem-ical Physics143, 084111 (2015).
10. Faber F. A., Hutchison L., Huang B., Gilmer J., Schoenholz S. S., Dahl G. E., Vinyals O., Kearnes S., Riley P. F., von Lilienfeld O. A. Journal of Chemical Theory and Computation13, 5255 (2017).
11. Ghosh K., Stuke A., Todorovi´c M., Jørgensen P. B., Schmidt M. N., Vehtari A., Rinke P. Advanced Science6, 1970053 (2019).
12. Becke A. D. The Journal of Chemical Physics98, 1372 (1993).
13. Zerner M. C. Reviews in Computational Chemistry2, 313 (1991).
14. Kohn W., Sham L. J. Physical Review140, A1133 (1965).
15. Runge E., Gross E. K. Physical Review Letters52, 997 (1984).
16. Leang S. S., Zahariev F., Gordon M. S. The Journal of Chemical Physics136, 104101 (2012).
17. Silva-Junior M. R., Schreiber M., Sauer S. P., Thiel W. The Journal of Chemical Physics129, 104103 (2008).
18. Thiel W. Wiley Interdisciplinary Reviews: Computational Molecular Science4, 145 (2014).
19. Khan M. S., Khan Z. H. Canadian Journal of Analytical Sciences and Spectroscopy47, 146 (2002).
20. Budyka M. F. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy207, 1 (2019).
21. Silva-Junior M. R., Thiel W. Journal of Chemical Theory and Computation6, 1546 (2010).
22. Suendo V., Viridi S. ITB Journal of Science44A, 79 (2012).
23. Wegermann C. A., da Rocha J. C., Drechsel S. M., Nunes F. S. Dyes and Pigments99, 839 (2013).
24. Saraha A. R., Rakhman K. A., Sugrah N. Asian Journal of Chemistry30, 1057 (2018).
25. Kim S., Chen J., Cheng T., Gindulyte A., He J., He S., Li Q., Shoemaker B. A., Thiessen P. A., Yu B., Zaslavsky L., Zhang J., Bolton E. E. Nucleic Acids Research47, D1102 (2018).
26. Breiman L. Machine Learning45, 5 (2001).
27. Cutler A., Cutler D. R., Stevens J. R. inEnsemble Machine Learning (Springer, 2012) pp. 157–175.
28. Zhang W., Wu C. inInternational Conference on Information Technology in Geo-Engineering (Springer, 2019) pp. 243–255.
29. Liaw A., Wiener M. R News2, 18 (2002).
30. Svetnik V., Liaw A., Tong C., Culberson J. C., Sheridan R. P., Feuston B. P. Journal ofChemical Information and Computer Sciences43, 1947 (2003).
31. Van Der Walt S., Colbert S. C., Varoquaux G. Computing in Science & Engineering13, 22 (2011).
32. Virtanen P., Gommers R., Oliphant T. E., et al. Nature Methods, 1 (2020).
33. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blon-del M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay ´E. Journal of Machine Learning Research12, 2825 (2011).
34. Hunter J. D. Computing in Science & Engineering9, 90 (2007).
35. Neese F. Wiley Interdisciplinary Reviews: Computational Molecular Science8, e1327 (2018).
36. Willighagen E. “SMILES Depicter,” https://www. simolecule. com/cdkdepict/depict. html (2019), [Online; accessed 19-December-2019].
37. Zasso M. “SMILES Generator, ”http://www. cheminfo. org/flavor/malaria/Utilities/SMILES_generator___checker/index. html (2019), [Online; accessed 19-December-2019].
Review
For citations:
Shakirov M.A. Calibrating optical spectra using machine learning algorithms. Magnetic Resonance in Solids. 2020;22(1):20102 (11 pp.). https://doi.org/10.26907/mrsej-20102
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