Advertisement

A neural network protocol for predicting molecular bond energy

  • Chao Feng
  • Edward Sharman
  • Sheng Ye
  • Yi Luo
  • Jun JiangEmail author
Articles
  • 13 Downloads

Abstract

Molecular bond energy is a key parameter for analyzing the properties of chemical activity, stability and flexibility. Calculating bond energy is a challenge due to the cost of first-principles simulations and unsatisfactory prediction using empirical formula. Here we show that a neural network (NN) machine-learning method can achieve quick prediction of bond energies of organic molecules. Using atomic species and charge information as descriptors, we trained a NN protocol and applied it to predict the bond energy in a certain chemical bond that agreed with density functional theory calculations. This protocol also provided a way to evaluate the effects of different methods of atomic charge analysis on NN training. Trained to accurately estimate bond energies, this NN protocol provides a cost-effective tool for optimizing chemical reactions, accelerating molecular design, and other important applications.

Keywords

bond energy neural network density functional theory 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2017YFA0303500, 2018YFA0208603), the National Natural Science Foundation of China (21633006, 21633007, 21790350), the Fundamental Research Funds for the Central Universities (WK2340000072). The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of University of Science and Technology of China and the National Supercomputing Center in Changsha.

References

  1. 1.
    Cheng GJ, Zhang X, Chung LW, Xu L, Wu YD. J Am Chem Soc, 2015, 137: 1706–1725CrossRefGoogle Scholar
  2. 2.
    Rohling RY, Tranca IC, Hensen EJM, Pidko EA. J Phys Chem C, 2019, 123: 2843–2854CrossRefGoogle Scholar
  3. 3.
    Romanova A, Lyssenko K, Ananyev I. J Comput Chem, 2018, 39: 1607–1616CrossRefGoogle Scholar
  4. 4.
    Manz TA. RSC Adv, 2017, 7: 45552–45581CrossRefGoogle Scholar
  5. 5.
    Hoffmann R. Acc Chem Res, 1971, 4: 1–9CrossRefGoogle Scholar
  6. 6.
    Dronskowski R, Bloechl PE. J Phys Chem, 1993, 97: 8617–8624CrossRefGoogle Scholar
  7. 7.
    Wheatley RJ, Gopal AA. Phys Chem Chem Phys, 2012, 14: 2087CrossRefGoogle Scholar
  8. 8.
    Maintz S, Deringer VL, Tchougréeff AL, Dronskowski R. J Comput Chem, 2016, 37: 1030–1035CrossRefGoogle Scholar
  9. 9.
    Pieniazek S, Clemente F, Houk K. Angew Chem, 2008, 120: 7860–7863CrossRefGoogle Scholar
  10. 10.
    Wodrich MD, Corminboeuf C, Wheeler SE. J Phys Chem A, 2012, 116: 3436–3447CrossRefGoogle Scholar
  11. 11.
    Stein SE, Brown RL. J Am Chem Soc, 1991, 113: 787–793CrossRefGoogle Scholar
  12. 12.
    Kurita E, Matsuura H, Ohno K. Spectrochim Acta A-Mol Biomol Spectr, 2004, 60: 3013–3023CrossRefGoogle Scholar
  13. 13.
    Gordy W. J Chem Phys, 1947, 15: 305–310CrossRefGoogle Scholar
  14. 14.
    Kraka E, Setiawan D, Cremer D. J Comput Chem, 2016, 37: 130–142CrossRefGoogle Scholar
  15. 15.
    Kaupp M, Danovich D, Shaik S. Coord Chem Rev, 2017, 344: 355–362CrossRefGoogle Scholar
  16. 16.
    Montavon G, Rupp M, Gobre V, Vazquez-Mayagoitia A, Hansen K, Tkatchenko A, Müller KR, Anatole von Lilienfeld O. New J Phys, 2013, 15: 095003CrossRefGoogle Scholar
  17. 17.
    Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Nature, 2018, 559: 547–555CrossRefGoogle Scholar
  18. 18.
    Li R, Li B, Zhang G, Jiang J, Luo Y. Chin J Chem Phys, 2018, 31: 341–349CrossRefGoogle Scholar
  19. 19.
    Gardner MW, Dorling SR. Atmos Environ, 1998, 32: 2627–2636CrossRefGoogle Scholar
  20. 20.
    Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. J Chem Inf Comput Sci, 2003, 43: 1947–1958CrossRefGoogle Scholar
  21. 21.
    Wiberg KB, Rablen PR. J Comput Chem, 1993, 14: 1504–1518CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Chao Feng
    • 1
  • Edward Sharman
    • 2
  • Sheng Ye
    • 1
  • Yi Luo
    • 1
  • Jun Jiang
    • 1
    Email author
  1. 1.Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemistry and Materials ScienceUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Department of NeurologyUniversity of CaliforniaIrvineUSA

Personalised recommendations