A generalized method for the estimation of the intensity of electron-phonon interaction in photosynthetic pigments using the evolutionary optimization algorithm

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

Modeling of the optical response of photosynthetic pigments is an essential part of the study of fundamental physical processes of interaction of multi-atomic molecules with an external electromagnetic field. The use of semiclassical quantum theories in this case is more preferable than the use of ab initio methods for calculating the ground and excited states of a molecule, since semiclassical theories allow us to use characteristic functions, such as spectral density, to calculate absorption spectra rather than to take into account the full set of electron and atom configurations. The main disadvantage of this approach is the necessity of constant comparison of the calculated and experimental spectra and, as a consequence, the need to justify the uniqueness of the obtained parameters of the system under study and to evaluate their statistical significance. One of the possible options to significantly improve the quality of the optical response calculation is the use of a heuristic evolutionary optimization algorithm that minimizes the difference between the measured and theoretical spectra by determining the most appropriate set of model parameters. Using the spectra of photosynthetic pigments measured in different solvents as an example, we have shown that the modeling optimization not only allows us to obtain a good agreement between the calculated and experimental data, but also to unambiguously determine such parameters of the theory as the electron-phonon interaction coefficients for the electronic excited states of chlorophyll, lutein and β-carotene.

作者简介

V. Kurkov

Prokhorov General Physics Institute of the Russian Academy of Sciences; Moscow Institute of Physics and Technology (National Research University)

Email: rpishchal@kapella.gpi.ru
俄罗斯联邦, Moscow; Dolgoprudny

D. Chesalin

Prokhorov General Physics Institute of the Russian Academy of Sciences

Email: rpishchal@kapella.gpi.ru
俄罗斯联邦, Moscow

A. Razjivin

Lomonosov Moscow State University

Email: rpishchal@kapella.gpi.ru

Belozersky Research Institute of Physico-Chemical Biology

俄罗斯联邦, Moscow

U. Shkirina

Prokhorov General Physics Institute of the Russian Academy of Sciences; Lomonosov Moscow State University

Email: rpishchal@kapella.gpi.ru

Department of Mechanics and Mathematics

俄罗斯联邦, Moscow; Moscow

R. Pishchalnikov

Prokhorov General Physics Institute of the Russian Academy of Sciences

编辑信件的主要联系方式.
Email: rpishchal@kapella.gpi.ru
俄罗斯联邦, Moscow

参考

  1. Jang S. J., and Mennucci B. Rev. Mod. Phys. 90, 035003 (2018). https://doi.org/10.1103/RevModPhys.90.035003
  2. Mirkovic T., Ostroumov E. E., Anna J. M. et al. Chem. Rev. 117, 249 (2017). https://doi.org/10.1021/acs.chemrev.6b00002
  3. Gorokhov V.V., Knox P.P., Korvatovsky B.N. et al. Russ. J. Phys. Chem. B 17, 571 (2023). https://doi.org/10.1134/S199079312303020X
  4. Blankenship R.E. Molecular Mechanisms of Photosynthesis, 2nd ed., Wiley-Blackwell, Oxford, (2014).
  5. Renger T., Madjet M.E.A., Busch M.S.A. et al. Photosynth. Res. 116, 367 (2013). https://doi.org/10.1007/s11120-013-9893-3.
  6. Cherepanov D.A., Milanovsky G.E., Aybush A.V. et al. Russ. J. Phys. Chem. B 17, 584 (2023). https://doi.org/10.1134/S1990793123030181
  7. Renger T.J. Phys. Chem. B. 125, 6406 (2021). https://doi.org/10.1021/acs.jpcb.1c01479.
  8. Novoderezhkin V.I., Romero E., Dekker J.P. et al. ChemPhysChem. 12, 681 (2011). https://doi.org/10.1002/cphc.201000830.
  9. Bruggemann B., Sznee K., Novoderezhkin V. et al. J. Phys. Chem. B. 108, 13536 (2004). https://doi.org/10.1021/jp0401473
  10. Brixner T., Hildner R., Kohler J. et al. Adv. Energy Mater. 7, 1700236 (2017). https://doi.org/10.1002/aenm.201700236
  11. Croce R., and van Amerongen H. Nature Chemical Biology. 10, 492 (2014). https://doi.org/10.1038/nchembio.1555
  12. Cherepanov D.A., Milanovsky G.E., Nadtochenko V.A. et al. Russ. J. Phys. Chem. B 17, 594 (2023). https://doi.org/10.1134/S1990793123030193
  13. Nelson T.R., White A.J., Bjorgaard J.A. et al. Chem. Rev. 120, 2215 (2020). https://doi.org/10.1021/acs.chemrev.9b00447.
  14. Cremer D.. Pople J.A.J. Am. Chem. Soc. 97, 1354 (1975). https://doi.org/10.1021/ja00839a011
  15. Ditchfield R., Hehre W.J., Pople J.A.J. Chem. Phys. 54, 724 (1971). https://doi.org/10.1063/1.1674902
  16. Khrenova M.G., Polyakov I.V., Nemukhin A.V. Russ. J. Phys. Chem. B 16, 455 (2022). https://doi.org/10.1134/S1990793122030174
  17. Mukamel S. Principles of Nonlinear Optical Spectroscopy, Oxford University Press, New York, (1995).
  18. Chesalin D.D., Kulikov E.A., Yaroshevich I.A. et al. Swarm Evol. Comput. 75, 101210 (2022). https://doi.org/10.1016/j.swevo.2022.101210
  19. Storn R. IEEE Trans. Evol. Comput. 3, 22 (1999). https://doi.org/10.1109/4235.752918
  20. Storn R., Price K. J. Glob. Opt. 11, 341 (1997). https://doi.org/10.1023/A:1008202821328
  21. Opara K.R., Arabas J. Swarm Evol. Comput. 44, 546 (2019). https://doi.org/10.1016/j.swevo.2018.06.010
  22. Gudkov S.V., Sarimov R.M., Astashev M.E. et al. Phys. Usp. 67, 194 (2024). https://doi.org/10.3367/UFNe.2023.09.039577
  23. Pishchalnikov R.Y., Yaroshevich I.A., Zlenko D.V. et al. Photosynth. Res. 156, 3 (2023). https://doi.org/10.1007/s11120-022-00955-2
  24. Pishchalnikov R.Y., Yaroshevich I.A., Slastnikova T.A. et al. Phys. Chem. Chem. Phys. 21, 25707 (2019). https://doi.org/10.1039/c9cp04508b
  25. Balevičius V., Abramavicius D., Polívka T. J. Phys. Chem. Lett. 7, 3347 (2016). https://doi.org/10.1021/acs.jpclett.6b01455
  26. Uragami C., Saito K., Yoshizawa M., Molnar P. et al. Arch. Biochem. Biophys. 650, 49 (2018). https://doi.org/10.1016/j.abb.2018.04.021

补充文件

附件文件
动作
1. JATS XML

版权所有 © Russian Academy of Sciences, 2024