Carlos R Baiz1, Bartosz Błasiak2, Jens Bredenbeck3, Minhaeng Cho4,5, Jun-Ho Choi6, Steven A Corcelli7, Arend G Dijkstra8, Chi-Jui Feng9, Sean Garrett-Roe10, Nien-Hui Ge11, Magnus W D Hanson-Heine12, Jonathan D Hirst12, Thomas L C Jansen13, Kijeong Kwac4, Kevin J Kubarych14, Casey H Londergan15, Hiroaki Maekawa11, Mike Reppert16, Shinji Saito17, Santanu Roy18, James L Skinner19, Gerhard Stock20, John E Straub21, Megan C Thielges22, Keisuke Tominaga23, Andrei Tokmakoff9, Hajime Torii24, Lu Wang25, Lauren J Webb26, Martin T Zanni27. 1. Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States. 2. Department of Physical and Quantum Chemistry, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland. 3. Johann Wolfgang Goethe-University, Institute of Biophysics, Max-von-Laue-Strasse 1, 60438 Frankfurt am Main, Germany. 4. Center for Molecular Spectroscopy and Dynamics, Seoul 02841, Republic of Korea. 5. Department of Chemistry, Korea University, Seoul 02841, Republic of Korea. 6. Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea. 7. Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States. 8. School of Chemistry and School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, U.K. 9. Department of Chemistry, James Franck Institute and Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, United States. 10. Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States. 11. Department of Chemistry, University of California at Irvine, Irvine, California 92697-2025, United States. 12. School of Chemistry, University of Nottingham, Nottingham, University Park, Nottingham NG7 2RD, U.K. 13. University of Groningen, Zernike Institute for Advanced Materials, Nijenborgh 4, 9747 AG Groningen, The Netherlands. 14. Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States. 15. Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States. 16. Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada. 17. Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, Myodaiji, Okazaki 444-8585, Japan. 18. Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6110, United States. 19. Institute for Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States. 20. Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany. 21. Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States. 22. Department of Chemistry, Indiana University, 800 East Kirkwood, Bloomington, Indiana 47405, United States. 23. Molecular Photoscience Research Center, Kobe University, Nada, Kobe 657-0013, Japan. 24. Department of Applied Chemistry and Biochemical Engineering, Faculty of Engineering, and Department of Optoelectronics and Nanostructure Science, Graduate School of Science and Technology, Shizuoka University, 3-5-1 Johoku, Naka-Ku, Hamamatsu 432-8561, Japan. 25. Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, New Jersey 08854, United States. 26. Department of Chemistry, The University of Texas at Austin, 105 East 24th Street, STOP A5300, Austin, Texas 78712, United States. 27. Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706-1396, United States.
Abstract
Vibrational spectroscopy is an essential tool in chemical analyses, biological assays, and studies of functional materials. Over the past decade, various coherent nonlinear vibrational spectroscopic techniques have been developed and enabled researchers to study time-correlations of the fluctuating frequencies that are directly related to solute-solvent dynamics, dynamical changes in molecular conformations and local electrostatic environments, chemical and biochemical reactions, protein structural dynamics and functions, characteristic processes of functional materials, and so on. In order to gain incisive and quantitative information on the local electrostatic environment, molecular conformation, protein structure and interprotein contacts, ligand binding kinetics, and electric and optical properties of functional materials, a variety of vibrational probes have been developed and site-specifically incorporated into molecular, biological, and material systems for time-resolved vibrational spectroscopic investigation. However, still, an all-encompassing theory that describes the vibrational solvatochromism, electrochromism, and dynamic fluctuation of vibrational frequencies has not been completely established mainly due to the intrinsic complexity of intermolecular interactions in condensed phases. In particular, the amount of data obtained from the linear and nonlinear vibrational spectroscopic experiments has been rapidly increasing, but the lack of a quantitative method to interpret these measurements has been one major obstacle in broadening the applications of these methods. Among various theoretical models, one of the most successful approaches is a semiempirical model generally referred to as the vibrational spectroscopic map that is based on a rigorous theory of intermolecular interactions. Recently, genetic algorithm, neural network, and machine learning approaches have been applied to the development of vibrational solvatochromism theory. In this review, we provide comprehensive descriptions of the theoretical foundation and various examples showing its extraordinary successes in the interpretations of experimental observations. In addition, a brief introduction to a newly created repository Web site (http://frequencymap.org) for vibrational spectroscopic maps is presented. We anticipate that a combination of the vibrational frequency map approach and state-of-the-art multidimensional vibrational spectroscopy will be one of the most fruitful ways to study the structure and dynamics of chemical, biological, and functional molecular systems in the future.
Vibclass="Chemical">rational spclass="Gene">an class="Chemical">ectroscopy is an essential tool in chemical analyses, biological assays, and studies of functional materials. Over the past decade, various coherent nonlinear vibrational spectroscopic techniques have been developed and enabled researchers to study time-correlations of the fluctuating frequencies that are directly related to solute-solvent dynamics, dynamical changes in molecular conformations and local electrostatic environments, chemical and biochemical reactions, protein structural dynamics and functions, characteristic processes of functional materials, and so on. In order to gain incisive and quantitative information on the local electrostatic environment, molecular conformation, protein structure and interprotein contacts, ligand binding kinetics, and electric and optical properties of functional materials, a variety of vibrational probes have been developed and site-specifically incorporated into molecular, biological, and material systems for time-resolved vibrational spectroscopic investigation. However, still, an all-encompassing theory that describes the vibrational solvatochromism, electrochromism, and dynamic fluctuation of vibrational frequencies has not been completely established mainly due to the intrinsic complexity of intermolecular interactions in condensed phases. In particular, the amount of data obtained from the linear and nonlinear vibrational spectroscopic experiments has been rapidly increasing, but the lack of a quantitative method to interpret these measurements has been one major obstacle in broadening the applications of these methods. Among various theoretical models, one of the most successful approaches is a semiempirical model generally referred to as the vibrational spectroscopic map that is based on a rigorous theory of intermolecular interactions. Recently, genetic algorithm, neural network, and machine learning approaches have been applied to the development of vibrational solvatochromism theory. In this review, we provide comprehensive descriptions of the theoretical foundation and various examples showing its extraordinary successes in the interpretations of experimental observations. In addition, a brief introduction to a newly created repository Web site (http://frequencymap.org) for vibrational spectroscopic maps is presented. We anticipate that a combination of the vibrational frequency map approach and state-of-the-art multidimensional vibrational spectroscopy will be one of the most fruitful ways to study the structure and dynamics of chemical, biological, and functional molecular systems in the future.
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