1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Physical Chemistry
  4. Volume 72, 2021
  5. Article

Annual Review of Physical Chemistry

Volume 72, 2021

Vibronic and Environmental Effects in Simulations of Optical Spectroscopy

Abstract

Including both environmental and vibronic effects is important for accurate simulation of optical spectra, but combining these effects remains computationally challenging. We outline two approaches that consider both the explicit atomistic environment and the vibronic transitions. Both phenomena are responsible for spectral shapes in linear spectroscopy and the electronic evolution measured in nonlinear spectroscopy. The first approach utilizes snapshots of chromophore-environment configurations for which chromophore normal modes are determined. We outline various approximations for this static approach that assumes harmonic potentials and ignores dynamic system-environment coupling. The second approach obtains excitation energies for a series of time-correlated snapshots. This dynamic approach relies on the accurate truncation of the cumulant expansion but treats the dynamics of the chromophore and the environment on equal footing. Both approaches show significant potential for making strides toward more accurate optical spectroscopy simulations of complex condensed phase systems.

Keyword(s): 2DES, cumulant, ensemble, Franck–Condon, nonlinear spectroscopy
Loading

Article metrics loading...

/content/journals/10.1146/annurev-physchem-090419-051350
2021-04-20
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/physchem/72/1/annurev-physchem-090419-051350.html?itemId=/content/journals/10.1146/annurev-physchem-090419-051350&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Isborn CM, Luehr N, Ufimtsev IS, Martínez TJ. 2011. Excited-state electronic structure with configuration interaction singles and Tamm–Dancoff time-dependent density functional theory on graphical processing units. J. Chem. Theory Comput. 7:1814–23
     [Google Scholar]
  2. 2. 
    Isborn CM, Götz AW, Clark MA, Walker RC, Martínez TJ. 2012. Electronic absorption spectra from MM and ab initio QM/MM molecular dynamics: environmental effects on the absorption spectrum of photoactive yellow protein. J. Chem. Theory Comput. 8:5092–106
     [Google Scholar]
  3. 3. 
    Prentice JC, Aarons J, Womack JC, Allen AE, Andrinopoulos L et al. 2020. The ONETEP linear-scaling density functional theory program. J. Chem. Phys. 152:174111
     [Google Scholar]
  4. 4. 
    Seritan S, Bannwarth C, Luehr N, Fales BS, Hohenstein EG et al. 2020. TeraChem: accelerating electronic structure and ab initio molecular dynamics with graphical processing units. J. Chem. Phys. 152:224110
     [Google Scholar]
  5. 5. 
    Warshel A, Levitt M. 1976. Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. J. Mol. Biol. 103:227–49
     [Google Scholar]
  6. 6. 
    Gao J. 1996. Hybrid quantum and molecular mechanical simulations: an alternative avenue to solvent effects in organic chemistry. Acc. Chem. Res. 29:298–305
     [Google Scholar]
  7. 7. 
    Senn HM, Thiel W. 2009. QM/MM methods for biomolecular systems. Angew. Chem. Int. Ed. 48:1198–229
     [Google Scholar]
  8. 8. 
    Curutchet C, Muñoz-Losa A, Monti S, Kongsted J, Scholes GD, Mennucci B. 2009. Electronic energy transfer in condensed phase studied by a polarizable QM/MM model. J. Chem. Theory Comput. 5:1838–48
     [Google Scholar]
  9. 9. 
    Olsen JM, Aidas K, Kongsted J. 2010. Excited states in solution through polarizable embedding. J. Chem. Theory Comput. 6:3721–34
     [Google Scholar]
  10. 10. 
    Caprasecca S, Curutchet C, Mennucci B. 2012. Toward a unified modeling of environment and bridge-mediated contributions to electronic energy transfer: a fully polarizable QM/MM/PCM approach. J. Chem. Theory Comput. 8:4462–73
     [Google Scholar]
  11. 11. 
    Olsen JMH, Steinmann C, Ruud K, Kongsted J. 2015. Polarizable density embedding: a new QM/QM/MM-based computational strategy. J. Phys. Chem. A 119:5344–55
     [Google Scholar]
  12. 12. 
    Bondanza M, Nottoli M, Cupellini L, Lipparini F, Mennucci B. 2020. Polarizable embedding QM/MM: the future gold standard for complex (bio)systems?. Phys. Chem. Chem. Phys. 22:14433–4812. Provides a good overview of polarizable embedding QM/MM and its application to spectroscopy and dynamics simulations.
     [Google Scholar]
  13. 13. 
    Hachmann J, Olivares-Amaya R, Jinich A, Appleton AL, Blood-Forsythe MA et al. 2014. Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry—the Harvard Clean Energy Project. Energy Environ. Sci. 7:698–704
     [Google Scholar]
  14. 14. 
    Ramakrishnan R, Dral PO, Rupp M, Von Lilienfeld OA 2015. Big data meets quantum chemistry approximations: the Δ-machine learning approach. J. Chem. Theory Comput. 11:2087–96
     [Google Scholar]
  15. 15. 
    Pereira F, Xiao K, Latino DARS, Wu C, Zhang Q, Aires-de Sousa J 2017. Machine learning methods to predict density functional theory B3LYP energies of HOMO and LUMO orbitals. J. Chem. Inf. Model. 57:11–21
     [Google Scholar]
  16. 16. 
    Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. 2018. Machine learning for molecular and materials science. Nature 559:547–55
     [Google Scholar]
  17. 17. 
    Xia R, Kais S. 2018. Quantum machine learning for electronic structure calculations. Nat. Commun. 9:4195
     [Google Scholar]
  18. 18. 
    Santoro F, Lami A, Improta R, Bloino J, Barone V. 2008. Effective method for the computation of optical spectra of large molecules at finite temperature including the Duschinsky and Herzberg–Teller effect: the band of porphyrin as a case study. J. Chem. Phys. 128:224311
     [Google Scholar]
  19. 19. 
    Bloino J, Biczysko M, Santoro F, Barone V. 2010. General approach to compute vibrationally resolved one-photon electronic spectra. J. Chem. Theory Comput. 6:1256–74
     [Google Scholar]
  20. 20. 
    Olbrich C, Strümpfer J, Schulten K, Kleinekathöfer U. 2011. Theory and simulation of the environmental effects on FMO electronic transitions. J. Phys. Chem. Lett. 14:1771–76
     [Google Scholar]
  21. 21. 
    Shim S, Rebentrost P, Valleau S, Aspuru-Guzik A. 2012. Atomistic study of the long-lived quantum coherences in the Fenna-Matthews-Olson complex. Biophys. J. 102:649–60
     [Google Scholar]
  22. 22. 
    Baiardi A, Bloino J, Barone V. 2013. General time dependent approach to vibronic spectroscopy including Franck–Condon, Herzberg–Teller, and Duschinsky effects. J. Chem. Theory Comput. 9:4097–11522. Showcases time-dependent approach for Franck–Condon calculations. Reference 64, de Souza et al., presents the more mathematically stable formulation.
     [Google Scholar]
  23. 23. 
    Mennucci B. 2015. Modeling absorption and fluorescence solvatochromism with QM/classical approaches. Int. J. Quantum Chem. 115:1202–8
     [Google Scholar]
  24. 24. 
    Lee MK, Bravaya KB, Coker DF. 2017. First-principles models for biological light-harvesting: phycobiliprotein complexes from cryptophyte algae. J. Am. Chem. Soc. 139:7803–14
     [Google Scholar]
  25. 25. 
    Mallus MI, Shakya Y, Prajapati JD, Kleinekathöfer U. 2018. Environmental effects on the dynamics in the light-harvesting complexes LH2 and LH3 based on molecular simulations. Chem. Phys. 515:141–51
     [Google Scholar]
  26. 26. 
    Blau SM, Bennett IG, Kreisbeck C, Scholes GD Aspuru-Guzik A. 2018. Local protein solvation drives direct down-conversion in phycobiliprotein PC645 via incoherent vibronic transport italicPNAS 115:E3342–50
     [Google Scholar]
  27. 27. 
    Cunningham K, Brand WC, Williams DF, Orlandi G. 1973. Vibronic intensity borrowing. Deuterium effect and emission—absorption asymmetry of the S1-S0 transition in pyrene. Chem. Phys. Lett. 20:496–500
     [Google Scholar]
  28. 28. 
    Avila Ferrer FJ, Improta R, Santoro F, Barone V 2011. Computing the inhomogeneous broadening of electronic transitions in solution: a first-principle quantum mechanical approach. Phys. Chem. Chem. Phys. 13:17007–12
     [Google Scholar]
  29. 29. 
    Nebgen B, Emmert FL, Slipchenko LV. 2012. Vibronic coupling in asymmetric bichromophores: theory and application to diphenylmethane. J. Chem. Phys. 137:084112
     [Google Scholar]
  30. 30. 
    Cerezo J, Avila Ferrer FJ, Prampolini G, Santoro F 2015. Modeling solvent broadening on the vibronic spectra of a series of coumarin dyes. From implicit to explicit solvent models. J. Chem. Theory Comput. 11:5810–25
     [Google Scholar]
  31. 31. 
    Santoro F, Jaquemin D. 2016. Going beyond the vertical approximation with time-dependent density functional theory. Wiley Interdiscip. Rev. Comput. Mol. Sci. 6:460–86
     [Google Scholar]
  32. 32. 
    Zakaraya MG, Maisuradze GG, Ulstrup J. 1989. Theory of inhomogeneous environmental Gaussian broadening of resonance Raman excitation profiles for polyatomic molecules in solution. J. Raman Spectrosc. 20:359–65
     [Google Scholar]
  33. 33. 
    Avila Ferrer FJ, Cerezo J, Soto J, Improta R, Santoro F 2014. First-principle computation of absorption and fluorescence spectra in solution accounting for vibronic structure, temperature effects and solvent inhomogenous broadening. J. Chem. Theory Comput.1040–1041328–37
     [Google Scholar]
  34. 34. 
    Halpin A, Johnson PJ, Tempelaar R, Murphy RS, Knoester J et al. 2014. Two-dimensional spectroscopy of a molecular dimer unveils the effects of vibronic coupling on exciton coherences. Nat. Chem. 6:196–201
     [Google Scholar]
  35. 35. 
    Schlau-Cohen GS. 2015. Principles of light harvesting from single photosynthetic complexes. Interface Focus 5:20140088
     [Google Scholar]
  36. 36. 
    Dean JC, Scholes GD. 2017. Coherence spectroscopy in the condensed phase: insights into molecular structure, environment, and interactions. Acc. Chem. Res. 50:2746–55
     [Google Scholar]
  37. 37. 
    Zuehlsdorff TJ, Montoya-Castillo A, Napoli JA, Markland TE, Isborn CM. 2019. Optical spectra in the condensed phase: capturing anharmonic and vibronic features using dynamic and static approaches. J. Chem. Phys. 151:074111
     [Google Scholar]
  38. 38. 
    Zuehlsdorff TJ, Isborn CM. 2019. Modeling absorption spectra of molecules in solution. Int. J. Quantum Chem. 119:e25719
     [Google Scholar]
  39. 39. 
    Roberts ST, Loparo JJ, Tokmakoff A. 2006. Characterization of spectral diffusion from two-dimensional line shapes. J. Chem. Phys. 125:084502
     [Google Scholar]
  40. 40. 
    Cho M. 2008. Coherent two-dimensional optical spectroscopy. Chem. Rev. 108:1331–418
     [Google Scholar]
  41. 41. 
    Calhoun TR, Ginsberg NS, Schlau-Cohen GS, Cheng YC, Ballottari M et al. 2009. Quantum coherence enabled determination of the energy landscape in light-harvesting complex II. J. Phys. Chem. B 113:16291–95
     [Google Scholar]
  42. 42. 
    Rancova O, Abramavičius D. 2014. Static and dynamic disorder in bacterial light-harvesting complex LH2: a 2DES simulation study. J. Phys. Chem. B 118:7533–40
     [Google Scholar]
  43. 43. 
    Dean JC, Rafiq S, Oblinsky DG, Cassette E, Jumper CC, Scholes GD. 2015. Broadband transient absorption and two-dimensional electronic spectroscopy of methylene blue. J. Phys. Chem. A 119:9098–108
     [Google Scholar]
  44. 44. 
    Fuller FD, Ogilvie JP. 2015. Experimental implementations of two-dimensional Fourier transform electronic spectroscopy. Annu. Rev. Phys. Chem. 66:667–90
     [Google Scholar]
  45. 45. 
    Dean JC, Oblinsky DG, Rafiq S, Scholes GD. 2016. Methylene blue exciton states steer nonradiative relaxation: ultrafast spectroscopy of methylene blue dimer. J. Phys. Chem. B 120:440–54
     [Google Scholar]
  46. 46. 
    Lee Y, Das S, Malamakal RM, Meloni S, Chenoweth DM, Anna JM. 2017. Ultrafast solvation dynamics and vibrational coherences of halogenated boron-dipyrromethene derivatives revealed through two-dimensional electronic spectroscopy. J. Am. Chem. Soc. 139:14733–42
     [Google Scholar]
  47. 47. 
    Cipolloni M, Fresch B, Occhiuto I, Rukin P, Komarova KG et al. 2017. Coherent electronic and nuclear dynamics in a rhodamine heterodimer-DNA supramolecular complex. Phys. Chem. Chem. Phys 19:23043–51
     [Google Scholar]
  48. 48. 
    Anda A, Abramavičius D, Hansen T. 2018. Two-dimensional electronic spectroscopy of anharmonic molecular potentials. Phys. Chem. Chem. Phys. 20:1642–52
     [Google Scholar]
  49. 49. 
    Bolzonello L, Polo A, Volpato A, Meneghin E, Cordaro M et al. 2018. Two-dimensional electronic spectroscopy reveals dynamics and mechanisms of solvent-driven inertial relaxation in polar BODIPY dyes. J. Phys. Chem. Lett. 9:1079–85
     [Google Scholar]
  50. 50. 
    Maiuri M, Ostroumov EE, Saer RG, Blankenship RE, Scholes GD. 2018. Coherent wavepackets in the Fenna–Matthews–Olson complex are robust to excitonic-structure perturbations caused by mutagenesis. Nat. Chem. 10:177–83
     [Google Scholar]
  51. 51. 
    Franck J. 1925. Elementary processes of photochemical reactions. J. Trans. Faraday Soc. 21:536–42
     [Google Scholar]
  52. 52. 
    Condon E. 1926. A theory of intensity distribution in band systems. Phys. Rev. 28:1182–201
     [Google Scholar]
  53. 53. 
    Cederbaum LS, Domcke W. 1976. A many-body approach to the vibrational structure in molecular electronic spectra. I. Theory. J. Chem. Phys. 64:603–11
     [Google Scholar]
  54. 54. 
    Roche M. 1990. On the polyatomic Franck–Condon factors. Chem. Phys. Lett. 168:556–58
     [Google Scholar]
  55. 55. 
    Islampour R, Dehestani M, Lin SH. 1999. A new expression for multidimensional Franck–Condon integrals. J. Mol. Spectrosc. 194:179–84
     [Google Scholar]
  56. 56. 
    Toniolno A, Persico M. 1999. Efficient calculation of Franck–Condon factors and vibronic couplings in polyatomics. J. Comput. Chem. 22:968–75
     [Google Scholar]
  57. 57. 
    Luis JM, Torrent-Sucarrat M, Solà M. 2005. Calculation of Franck–Condon factors including anharmonicity: simulation of the band in the photoelectron spectrum of ethylene. J. Chem. Phys. 122:184104
     [Google Scholar]
  58. 58. 
    Small GJ. 1971. Herzberg–Teller vibronic coupling and the Duschinsky effect. J. Chem. Phys. 54:3300–6
     [Google Scholar]
  59. 59. 
    Santoro F, Barone V. 2009. Computational approach to the study of the lineshape of absorption and electronic circular dichroism spectra. J. Quantum Chem. 110:476–86
     [Google Scholar]
  60. 60. 
    Niu Y, Peng Q, Deng C, Gao X, Shuai Z. 2010. Theory of excited state decays and optical spectra: application to polyatomic molecules. J. Phys. Chem. A 114:7817–31
     [Google Scholar]
  61. 61. 
    Avila Ferrer FJ, Barone V, Cappelli C, Santoro F 2013. Duschinsky, Herzberg–Teller, and multiple electronic resonance interferential effects in resonance Raman spectra and excitation profiles. J. Chem. Theory Comput. 9:3597–611
     [Google Scholar]
  62. 62. 
    Mukazhanova A, Trerayapiwat KJ, Mazaheripour A, Wardrip AG, Frey NC et al. 2020. Accurate first-principles calculation of the vibronic spectrum of stacked perylene tetracarboxylic acid diimides. J. Phys. Chem. A 124:3055–63
     [Google Scholar]
  63. 63. 
    Li SL, Truhlar DG. 2017. Franck–Condon models for simulating the band shape of electronic absorption spectra. J. Chem. Theory Comput. 13:2823–30
     [Google Scholar]
  64. 64. 
    de Souza B, Neese F, Izsák R. 2018. On the theoretical prediction of fluorescence rates from first principles using the path integral approach. J. Chem. Phys. 148:034104
     [Google Scholar]
  65. 65. 
    Miertuš S, Scrocco E, Tomasi J. 1981. Electrostatic interaction of a solute with a continuum. A direct utilizaion of ab initio molecular potentials for the prevision of solvent effects. Chem. Phys. 55:117–29
     [Google Scholar]
  66. 66. 
    Cammi R, Tomasi J. 1995. Remarks on the use of the apparent surface charges (ASC) methods in solvation problems: iterative versus matrix-inversion procedures and the renormalization of the apparent charges. J. Comput. Chem 16:1449–58
     [Google Scholar]
  67. 67. 
    Cossi M, Scalmani G, Rega N, Barone V. 2002. New developments in the polarizable continuum model for quantum mechanical and classical calculations on molecules in solution. J. Chem. Phys. 117:43–54
     [Google Scholar]
  68. 68. 
    Scalmani G, Frisch MJ. 2010. Continuous surface charge polarizable continuum models of solvation. I. General formalism. J. Chem. Phys. 132:114110
     [Google Scholar]
  69. 69. 
    Mennucci B. 2012. Polarizable continuum model. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2:386–404
     [Google Scholar]
  70. 70. 
    Roux B, Simonson T. 1999. Implicit solvent models. Biophys. Chem. 78:1–20
     [Google Scholar]
  71. 71. 
    Cramer CJ, Truhlar DG. 1999. Implicit solvation models: equilibria, structure, spectra, and dynamics. Chem. Rev. 99:2161–200
     [Google Scholar]
  72. 72. 
    Coon J, DeWames R, Loyd C. 1962. The Franck–Condon principle and the structures of excited electronic states of molecules. J. Mol. Spectrosc. 8:285–99
     [Google Scholar]
  73. 73. 
    Malcioğlu OB, Calzolari A, Gebauer R, Varsano D, Baroni S. 2011. Dielectric and thermal effects on the optical properties of natural dyes: a case study on solvated cyanin. J. Am. Chem. Soc. 133:15425–33
     [Google Scholar]
  74. 74. 
    Retegan M, Neese F, Pantazis DA. 2013. Convergence of QM/MM and cluster models for the spectroscopic properties of the oxygen-evolving complex in photosystem II. J. Chem. Theory Comput. 9:3832–42
     [Google Scholar]
  75. 75. 
    De Mitri N, Monti S, Prampolini G, Barone V. 2013. Absorption and emission spectra of a flexible dye in solution: a computational time-dependent approach. J. Chem. Theory Comput. 9:4507–16
     [Google Scholar]
  76. 76. 
    Valsson O, Campomanes P, Tavernelli I, Rothlisberger U, Filippi C. 2013. Rhodopsin absorption from first principles: bypassing common pitfalls. J. Chem. Theory Comput. 9:2441–54
     [Google Scholar]
  77. 77. 
    Provorse MR, Peev T, Xiong C, Isborn CM. 2016. Convergence of excitation energies in mixed quantum and classical solvent: comparison of continuum and point charge models. J. Phys. Chem. B 120:12148–59
     [Google Scholar]
  78. 78. 
    Zuehlsdorff TJ, Haynes PD, Hanke F, Payne MC, Hine NDM. 2016. Solvent effects on electronic excitations of an organic chromophore. J. Chem. Theory Comput. 12:1853–61
     [Google Scholar]
  79. 79. 
    Tapavicza E, Furche F, Sundholm D. 2016. Importance of vibronic effects in the UV-Vis spectrum of the 7,7,8,8-tetracyanoquinodimethane anion. J. Chem. Theory Comput. 12:5058–66
     [Google Scholar]
  80. 80. 
    Zuehlsdorff TJ, Haynes PD, Payne MC, Hine NDM. 2017. Predicting solvatochromic shifts and colours of a solvated organic dye: the example of Nile red. J. Chem. Phys. 146:124504
     [Google Scholar]
  81. 81. 
    Milanese JM, Provorse MR, Alameda E, Isborn CM 2017. Convergence of computed aqueous absorption spectra with explicit quantum mechanical solvent. J. Chem. Theory Comput. 13:2159–71
     [Google Scholar]
  82. 82. 
    Walker RC, Crowley MF, Case DA. 2007. The implementation of a fast and accurate QM/MM potential method in Amber. J. Comput. Chem. 29:1019–31
     [Google Scholar]
  83. 83. 
    Senthilkumar K, Mujika JI, Ranaghan KE, Manby FR, Mulholland AJ, Harvey JN. 2008. Analysis of polarization in QM/MM modelling of biologically relevant hydrogen bonds. J. R. Soc. Interface 5:S207–16
     [Google Scholar]
  84. 84. 
    Beierlein FR, Michel J, Essex JW. 2011. A simple QM/MM approach for capturing polarization effects in protein-ligand binding free energy calculations. J. Phys. Chem. B 115:4911–26
     [Google Scholar]
  85. 85. 
    Jurinovich S, Viani L, Prandi IG, Renger T, Mennucci B. 2015. Towards an ab initio description of the optical spectra of light-harvesting antennae: application to the CP29 complex of photosystem II. Phys. Chem. Chem. Phys. 17:14405–16
     [Google Scholar]
  86. 86. 
    Kulik HJ, Zhang J, Klinman JP, Martinez TJ. 2016. How large should the QM region be in QM/MM calculations? The case of catechol O-methyltransferase. J. Phys. Chem. B 120:11381–94
     [Google Scholar]
  87. 87. 
    Karelina M, Kulik HJ. 2017. Systematic quantum mechanical region determination in QM/MM simulation. J. Chem. Theory Comput. 13:563–76
     [Google Scholar]
  88. 88. 
    Loco D, Jurinovich S, Cupellini L, Menger MFSJ, Mennucci B. 2018. The modeling of the absorption lineshape for embedded molecules through a polarizable QM/MM approach. Photochem. Photobiol. Sci. 17:552–60
     [Google Scholar]
  89. 89. 
    Zuehlsdorff TJ, Napoli JA, Milanese JM, Markland TE, Isborn CM. 2018. Unraveling electronic absorption spectra using nuclear quantum effects: photoactive yellow protein and green fluorescent protein chromophores in water. J. Chem. Phys. 149:024107
     [Google Scholar]
  90. 90. 
    Zuehlsdorff TJ, Isborn CM. 2018. Combining the ensemble and Franck–Condon approaches for calculating spectral shapes of molecules in solution. J. Chem. Phys. 148:024110
     [Google Scholar]
  91. 91. 
    Shedge SV, Zuehlsdorff TJ, Servis MJ, Clark AE, Isborn CM. 2019. Effect of ions on the optical absorption spectra of aqueously solvated chromophores. J. Phys. Chem. A 123:6175–84
     [Google Scholar]
  92. 92. 
    Zuehlsdorff TJ, Hong H, Shi L, Isborn CM. 2020. Influence of electronic polarization on the spectral density. J. Phys. Chem. B 124:531–43
     [Google Scholar]
  93. 93. 
    Horton JT, Allen AEA, Dodda LS, Cole DJ. 2019. QUBEKit: automating the derivation of force field parameters from quantum mechanics. J. Chem. Inf. Model. 59:1366–81
     [Google Scholar]
  94. 94. 
    Behler J, Parrinello M. 2007. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98:146401
     [Google Scholar]
  95. 95. 
    Bartók AP, Payne MC, Kondor R, Csányi G. 2010. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104:136403
     [Google Scholar]
  96. 96. 
    Wang LP, Martinez TJ, Pande VS. 2014. Building force fields: an automatic, systematic, and reproducible approach. J. Phys. Chem. Lett. 5:1885–91
     [Google Scholar]
  97. 97. 
    Khorshidi A, Peterson AA. 2016. Amp: a modular approach to machine learning in atomistic simulations. Comput. Phys. Commun. 207:310–24
     [Google Scholar]
  98. 98. 
    Artrith N, Urban A. 2016. An implementation of artificial neural-network potentials for atomistic materials simulations: performance for TiO2. Comput. Mater. Sci. 114:135–50
     [Google Scholar]
  99. 99. 
    Wang LP, McKiernan KA, Gomes J, Beauchamp KA, Head-Gordon T et al. 2017. Building a more predictive protein force field: a systematic and reproducible route to AMBER-FB15. J. Phys. Chem. B 121:4023–39
     [Google Scholar]
  100. 100. 
    Chen MS, Zuehlsdorff TJ, Morawietz T, Isborn CM, Markland TE. 2020. Exploiting machine learning to efficiently predict multidimensional optical spectra in complex environments. J. Phys. Chem. Lett. 11:7559–68
     [Google Scholar]
  101. 101. 
    Marx D, Parrinello M. 1996. Ab initio path integral molecular dynamics: basic ideas. J. Chem. Phys. 104:4077–82
     [Google Scholar]
  102. 102. 
    Svoboda O, Ončák M, Slavíček P. 2011. Simulations of light induced processes in water based on ab initio path integrals molecular dynamics. II. Photoionization. J. Chem. Phys. 135:154302
     [Google Scholar]
  103. 103. 
    Ceriotti M, Parrinello M, Markland TE, Manolopoulos DE. 2010. Efficient stochastic thermostatting of path integral molecular dynamics. J. Chem. Phys. 133:124104
     [Google Scholar]
  104. 104. 
    Markland TE, Ceriotti M. 2018. Nuclear quantum effects enter the mainstream. Nat. Rev. Chem. 2:0109
     [Google Scholar]
  105. 105. 
    Tuckerman ME, Ungar PJ, von Rosenvinge T, Klein ML. 1996. Ab initio molecular dynamics simulations. J. Phys. Chem. 100:12878–87
     [Google Scholar]
  106. 106. 
    Tozzini V, Nifosì R. 2001. Ab initio molecular dynamics of the green fluorescent protein (GFP) chromophore: an insight into the photoinduced dynamics of green fluorescent proteins. J. Phys. Chem. B 105:5797–803
     [Google Scholar]
  107. 107. 
    Ko C, Levine B, Toniolo A, Manohar L, Olsen S et al. 2003. Ab initio excited-state dynamics of the photoactive yellow protein chromophore. J. Am. Chem. Soc 125:12710–11
     [Google Scholar]
  108. 108. 
    Weingart O, Schapiro I, Buss V. 2007. Photochemistry of visual pigment chromophore models by ab initio molecular dynamics. J. Phys. Chem. B 111:3782–88
     [Google Scholar]
  109. 109. 
    Zwier MC, Shorb JM, Krueger BP. 2007. Hybrid molecular dynamics-quantum mechanics simulations of solute spectral properties in the condensed phase: evaluation of simulation parameters. J. Comput. Chem. 28:1572–81
     [Google Scholar]
  110. 110. 
    Rosnik AM, Curutchet C. 2015. Theoretical characterization of the spectral density of the water-soluble chlorophyll-binding protein from combined quantum mechanics/molecular mechanics molecular dynamics simulations. J. Chem. Theory Comput. 11:5826–37
     [Google Scholar]
  111. 111. 
    Chandrasekaran S, Aghtar M, Valleau S, Aspuru-Guzik A, Kleinekathöfer U. 2015. Influence of force fields and quantum chemistry approach on spectral densities of BChl a in solution and in FMO proteins. J. Phys. Chem. B 119:9995–10004
     [Google Scholar]
  112. 112. 
    Kim CW, Park JW, Rhee YM. 2015. Effect of chromophore potential model on the description of exciton–phonon interactions. J. Phys. Chem. Lett. 6:2875–80
     [Google Scholar]
  113. 113. 
    Lee MK, Coker DF. 2016. Modeling electronic-nuclear interactions for excitation energy transfer processes in light-harvesting complexes. J. Phys. Chem. Lett. 7:3171–78
     [Google Scholar]
  114. 114. 
    Andreussi O, Prandi IG, Canpetella M, Prampolini G, Mennucci B. 2017. Classical force fields tailored for QM applications: Is it really a feasible strategy. ? J. Chem. Theory Comput. 13:4636–48
     [Google Scholar]
  115. 115. 
    Ferrer FJA, Davari MD, Morozov D, Groenhof G, Santoro F. 2014. The lineshape of the electronic spectrum of the green fluorescent protein chromophore, part II: solution phase. Chem. Phys. Chem. 15:3246–57
     [Google Scholar]
  116. 116. 
    Zaleśny R, Murugan NA, Gel'mukhanov F, Rinkevicius Z, Ośmiałowski B et al. 2015. Toward fully nonempirical simulations of optical band shapes of molecules in solution: a case study of heterocyclic ketoimine difluoroborates. J. Phys. Chem. A 119:5145–52
     [Google Scholar]
  117. 117. 
    Loco D, Cupellini L. 2018. Modeling the absorption lineshape of embedded systems from molecular dynamics: a tutorial review. Int. J. Quantum Chem. 119:e25726 117. A tutorial review that provides a good starting point for spectroscopy calculations within the dynamic approach.
     [Google Scholar]
  118. 118. 
    Cerezo J, Aranda D, Avila Ferrer FJ, Prampolini G, Santoro F 2020. Adiabatic-molecular dynamics generalized vertical Hessian approach: a mixed quantum classical method to compute electronic spectra of flexible molecules in the condensed phase. J. Chem. Theory Comput. 16:1215–31118. Applies the sum-FC method within a vertical Hessian approach for additional computational savings.
     [Google Scholar]
  119. 119. 
    Del Galdo S, Fusè M, Barone V. 2020. The ONIOM/PMM model for effective yet accurate simulation of optical and chiroptical spectra in solution: camphorquinone in methanol as a case study. J. Chem. Theory Comput. 16:3294–306
     [Google Scholar]
  120. 120. 
    Fortino M, Collini E, Pedone A, Bloino J. 2020. Role of specific solute–solvent interactions on the photophysical properties of distyryl substituted BODIPY derivatives. Phys. Chem. Chem. Phys. 22:10981–94
     [Google Scholar]
  121. 121. 
    Mukamel S. 1995. Principles of Nonlinear Optical Spectroscopy New York: Oxford Univ. Press121. Details many aspects of nonlinear spectroscopy. A more approachable set of notes by Peter Hamm, “Principles of Nonlinear Optical Spectroscopy: A Practical Approach,” can be found online.
  122. 122. 
    Mukamel S, Abramavicius D. 2004. Many-body approaches for simulating coherent nonlinear spectroscopies for electronic and vibrational excitons. Chem. Rev. 104:2073–98
     [Google Scholar]
  123. 123. 
    Li B, Johnson AE, Mukamel S, Myers AB. 1994. The Brownian oscillator model for solvation effects in spontaneous light emission and their relationship to electron transfer. J. Am. Chem. Soc. 116:11039–47
     [Google Scholar]
  124. 124. 
    Duschinsky F. 1937. On the interpretation of electronic spectra of polyatomic molecules. Acta Physicochim. URSS 7:551
     [Google Scholar]
  125. 125. 
    Bader JS, Berne BJ. 1994. Quantum and classical relaxation rates from classical simulations. J. Chem. Phys. 100:8359
     [Google Scholar]
  126. 126. 
    Egorov SA, Everitt KF, Skinner JL. 1999. Quantum dynamics and vibrational relaxation. J. Phys. Chem. A 103:9494–99
     [Google Scholar]
  127. 127. 
    Kim H, Rossky PJ. 2002. Evaluation of quantum correlation functions from classical data. J. Phys. Chem. B 106:8240–47
     [Google Scholar]
  128. 128. 
    Jung KA, Videla PE, Batista VS. 2018. Inclusion of nuclear quantum effects for simulations of nonlinear spectroscopy. J. Chem. Phys. 148:244105
     [Google Scholar]
  129. 129. 
    Anda A, De Vico L, Hansen T 2016. Absorption and fluorescence lineshape theory for polynomial potentials. J. Chem. Theory Comput. 12:5979–89
     [Google Scholar]
  130. 130. 
    Avila Ferrer FJ, Santoro F 2012. Comparison of vertical and adiabatic harmonic approaches for the calculation of the vibrational structure of electronic spectra. Phys. Chem. Chem. Phys. 14:13549–63
     [Google Scholar]
  131. 131. 
    Davis MM, Helzer HB. 1966. Titrimetric and equilibrium studies using indicators related to Nile blue A. Anal. Chem. 38:451–61
     [Google Scholar]
  132. 132. 
    Yanai T, Tew DP, Handy NC. 2004. A new hybrid exchange-correlation functional using the Coulomb-attenuating method (CAM-B3LYP). Chem. Phys. Lett. 393:51–57
     [Google Scholar]
  133. 133. 
    Kubo R. 1963. The fluctuation-dissipation theorem. Rep. Prog. Phys. 59:1665–735
     [Google Scholar]
  134. 134. 
    Craig IR, Manolopoulos DE. 2004. Quantum statistics and classical mechanics: real time correlation functions from ring polymer molecular dynamics. J. Chem. Phys. 121:3368–73
     [Google Scholar]
  135. 135. 
    Ramirez R, Lopez-Ciudad T, Kumar PP, Marx D. 2004. Quantum corrections to classical time-correlation functions: hydrogen bonding and anharmonic floppy modes. J. Chem. Phys. 121:3973–83
     [Google Scholar]
  136. 136. 
    Valleau S, Eisfeld A, Aspuru-Guzik A. 2012. On the alternatives for bath correlators and spectral densities from mixed quantum-classical simulations. J. Chem. Phys. 137:224103
     [Google Scholar]
  137. 137. 
    Zuehlsdorff TJ, Hong H, Shi L, Isborn CM. 2020. Nonlinear spectroscopy in the condensed phase: the role of Duschinsky rotations and third order cumulant contributions. J. Chem. Phys. 153:044127
     [Google Scholar]
  138. 138. 
    Zuehlsdorff TJ, Hine NDM, Spencer JS, Harrison NM, Riley DJ, Haynes PD. 2013. Linear-scaling time-dependent density-functional theory in the linear response formalism. J. Chem. Phys 139:064104
     [Google Scholar]
  139. 139. 
    Zuehlsdorff TJ, Hine NDM, Payne MC, Haynes PD. 2015. Linear-scaling time-dependent density-functional theory beyond the Tamm–Dancoff approximation: obtaining efficiency and accuracy with in situ optimised local orbitals. J. Chem. Phys 143:204107
     [Google Scholar]
  140. 140. 
    Seritan S, Bannwarth C, Fales BS, Hohenstein EG, Isborn CM et al. 2020. TeraChem: a graphical processing unit-accelerated electronic structure package for large-scale ab initio molecular dynamics. WIREs Comput. Mol. Sci. 26 July 2020.e1494
     [Google Scholar]
/content/journals/10.1146/annurev-physchem-090419-051350
Loading
Vibronic and Environmental Effects in Simulations of Optical Spectroscopy
Annual Review of Physical Chemistry 72, 165 (2021); https://doi.org/10.1146/annurev-physchem-090419-051350
/content/journals/10.1146/annurev-physchem-090419-051350
/content/journals/10.1146/annurev-physchem-090419-051350
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error