Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction
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...
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| Published in | Chemical reviews Vol. 120; no. 15; pp. 7152 - 7218 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Chemical Society
12.08.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0009-2665 1520-6890 1520-6890 |
| DOI | 10.1021/acs.chemrev.9b00813 |
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| 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. |
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| AbstractList | 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. 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.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. 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 inter-protein 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 website (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. |
| Author | Cho, Minhaeng Tokmakoff, Andrei Thielges, Megan C Jansen, Thomas L. C Dijkstra, Arend G Skinner, James L Hirst, Jonathan D Saito, Shinji Choi, Jun-Ho Torii, Hajime Londergan, Casey H Błasiak, Bartosz Webb, Lauren J Zanni, Martin T Roy, Santanu Straub, John E Corcelli, Steven A Baiz, Carlos R Reppert, Mike Garrett-Roe, Sean Stock, Gerhard Hanson-Heine, Magnus W. D Kubarych, Kevin J Feng, Chi-Jui Tominaga, Keisuke Bredenbeck, Jens Ge, Nien-Hui Maekawa, Hiroaki Wang, Lu Kwac, Kijeong |
| AuthorAffiliation | University of Chicago School of Chemistry and School of Physics and Astronomy University of Groningen Rutgers University School of Chemistry Zernike Institute for Advanced Materials University of Toronto Institute for Molecular Engineering Department of Chemistry Department of Physical and Quantum Chemistry Department of Applied Chemistry and Biochemical Engineering, Faculty of Engineering, and Department of Optoelectronics and Nanostructure Science, Graduate School of Science and Technology Korea University Biomolecular Dynamics, Institute of Physics Center for Molecular Spectroscopy and Dynamics Chemical Sciences Division Molecular Photoscience Research Center The University of Texas at Austin University of Pittsburgh Institute of Biophysics Department of Theoretical and Computational Molecular Science Department of Chemistry and Biochemistry Chemical Physics Theory Group, Department of Chemistry Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine Department of Chemistry, J |
| AuthorAffiliation_xml | – name: Korea University – name: Department of Chemistry – name: Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine – name: Center for Molecular Spectroscopy and Dynamics – name: University of Toronto – name: Biomolecular Dynamics, Institute of Physics – name: Department of Applied Chemistry and Biochemical Engineering, Faculty of Engineering, and Department of Optoelectronics and Nanostructure Science, Graduate School of Science and Technology – name: Zernike Institute for Advanced Materials – name: Institute of Biophysics – name: The University of Texas at Austin – name: School of Chemistry and School of Physics and Astronomy – name: Department of Theoretical and Computational Molecular Science – name: Institute for Molecular Engineering – name: Chemical Sciences Division – name: Department of Chemistry, James Franck Institute and Institute for Biophysical Dynamics – name: Rutgers University – name: Chemical Physics Theory Group, Department of Chemistry – name: Department of Physical and Quantum Chemistry – name: University of Groningen – name: University of Chicago – name: Molecular Photoscience Research Center – name: Department of Chemistry and Biochemistry – name: University of Pittsburgh – name: School of Chemistry – name: 16 Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada – name: 27 Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706-1396, U.S.A – name: 23 Molecular Photoscience Research Center, Kobe University, Nada, Kobe 657-0013, Japan – name: 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 – name: 6 Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea – name: 8 School of Chemistry and School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, U.K – name: 7 Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, U.S.A – name: 18 Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6110, U.S.A – name: 4 Center for Molecular Spectroscopy and Dynamics, Seoul 02841, Republic of Korea – name: 20 Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany – name: 2 Department of Physical and Quantum Chemistry, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland – name: 9 Department of Chemistry, James Franck Institute and Institute for Biophysical Dynamics, University of Chicago, Chicago, IL 60637, U.S.A – name: 17 Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, Myodaiji, Okazaki, 444-8585, Japan – name: 21 Department of Chemistry, Boston University, Boston, MA 02215, U.S.A – name: 12 School of Chemistry, University of Nottingham, Nottingham, University Park, Nottingham, NG7 2RD, U.K – name: 3 Johann Wolfgang Goethe-University, Institute of Biophysics, Max-von-Laue-Str. 1, 60438, Frankfurt am Main, Germany – name: 5 Department of Chemistry, Korea University, Seoul 02841, Republic of Korea – name: 11 Department of Chemistry, University of California at Irvine, Irvine, CA 92697-2025, U.S.A – name: 14 Department of Chemistry, University of Michigan, 930 N. University Ave., Ann Arbor, MI 48109, U.S.A – name: 13 University of Groningen, Zernike Institute for Advanced Materials, Nijenborgh 4, 9747 AG Groningen, The Netherlands – name: 10 Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15260, U.S.A – name: 26 Department of Chemistry, The University of Texas at Austin, 105 E. 24th Street, STOP A5300, Austin, Texas 78712, U.S.A – name: 1 Department of Chemistry, University of Texas at Austin, Austin, TX 78712, U.S.A – name: 19 Institute for Molecular Engineering, University of Chicago, Chicago, IL 60637, U.S.A – name: 15 Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, U.S.A – name: 22 Department of Chemistry, Indiana University, 800 East Kirkwood, Bloomington, Indiana 47405, U.S.A – name: 25 Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, U.S.A |
| Author_xml | – sequence: 1 givenname: Carlos R orcidid: 0000-0003-0699-8468 surname: Baiz fullname: Baiz, Carlos R organization: Department of Chemistry – sequence: 2 givenname: Bartosz orcidid: 0000-0003-1968-3465 surname: Błasiak fullname: Błasiak, Bartosz organization: Department of Physical and Quantum Chemistry – sequence: 3 givenname: Jens orcidid: 0000-0003-1929-9092 surname: Bredenbeck fullname: Bredenbeck, Jens organization: Institute of Biophysics – sequence: 4 givenname: Minhaeng orcidid: 0000-0003-1618-1056 surname: Cho fullname: Cho, Minhaeng email: mcho@korea.ac.kr organization: Korea University – sequence: 5 givenname: Jun-Ho orcidid: 0000-0001-5237-5566 surname: Choi fullname: Choi, Jun-Ho organization: Department of Chemistry – sequence: 6 givenname: Steven A orcidid: 0000-0001-6451-4447 surname: Corcelli fullname: Corcelli, Steven A organization: Department of Chemistry and Biochemistry – sequence: 7 givenname: Arend G surname: Dijkstra fullname: Dijkstra, Arend G organization: School of Chemistry and School of Physics and Astronomy – sequence: 8 givenname: Chi-Jui orcidid: 0000-0002-4006-9489 surname: Feng fullname: Feng, Chi-Jui organization: Department of Chemistry, James Franck Institute and Institute for Biophysical Dynamics – sequence: 9 givenname: Sean orcidid: 0000-0001-6199-8773 surname: Garrett-Roe fullname: Garrett-Roe, Sean organization: University of Pittsburgh – sequence: 10 givenname: Nien-Hui surname: Ge fullname: Ge, Nien-Hui organization: Department of Chemistry – sequence: 11 givenname: Magnus W. D orcidid: 0000-0002-6709-297X surname: Hanson-Heine fullname: Hanson-Heine, Magnus W. D organization: School of Chemistry – sequence: 12 givenname: Jonathan D orcidid: 0000-0002-2726-0983 surname: Hirst fullname: Hirst, Jonathan D organization: School of Chemistry – sequence: 13 givenname: Thomas L. C orcidid: 0000-0001-6066-6080 surname: Jansen fullname: Jansen, Thomas L. C organization: Zernike Institute for Advanced Materials – sequence: 14 givenname: Kijeong surname: Kwac fullname: Kwac, Kijeong organization: Center for Molecular Spectroscopy and Dynamics – sequence: 15 givenname: Kevin J orcidid: 0000-0003-1152-4734 surname: Kubarych fullname: Kubarych, Kevin J organization: Department of Chemistry – sequence: 16 givenname: Casey H orcidid: 0000-0002-5257-559X surname: Londergan fullname: Londergan, Casey H organization: Department of Chemistry – sequence: 17 givenname: Hiroaki surname: Maekawa fullname: Maekawa, Hiroaki organization: Department of Chemistry – sequence: 18 givenname: Mike surname: Reppert fullname: Reppert, Mike organization: University of Toronto – sequence: 19 givenname: Shinji orcidid: 0000-0003-4982-4820 surname: Saito fullname: Saito, Shinji organization: Department of Theoretical and Computational Molecular Science – sequence: 20 givenname: Santanu orcidid: 0000-0001-6991-8205 surname: Roy fullname: Roy, Santanu organization: Chemical Sciences Division – sequence: 21 givenname: James L orcidid: 0000-0001-6939-9759 surname: Skinner fullname: Skinner, James L organization: University of Chicago – sequence: 22 givenname: Gerhard orcidid: 0000-0002-3302-3044 surname: Stock fullname: Stock, Gerhard organization: Biomolecular Dynamics, Institute of Physics – sequence: 23 givenname: John E surname: Straub fullname: Straub, John E organization: Department of Chemistry – sequence: 24 givenname: Megan C orcidid: 0000-0002-4520-6673 surname: Thielges fullname: Thielges, Megan C organization: Department of Chemistry – sequence: 25 givenname: Keisuke orcidid: 0000-0002-4680-2362 surname: Tominaga fullname: Tominaga, Keisuke organization: Molecular Photoscience Research Center – sequence: 26 givenname: Andrei orcidid: 0000-0002-2434-8744 surname: Tokmakoff fullname: Tokmakoff, Andrei organization: Department of Chemistry, James Franck Institute and Institute for Biophysical Dynamics – sequence: 27 givenname: Hajime orcidid: 0000-0002-6061-9599 surname: Torii fullname: Torii, Hajime organization: Department of Applied Chemistry and Biochemical Engineering, Faculty of Engineering, and Department of Optoelectronics and Nanostructure Science, Graduate School of Science and Technology – sequence: 28 givenname: Lu orcidid: 0000-0001-6230-1835 surname: Wang fullname: Wang, Lu organization: Rutgers University – sequence: 29 givenname: Lauren J orcidid: 0000-0001-9999-5500 surname: Webb fullname: Webb, Lauren J organization: The University of Texas at Austin – sequence: 30 givenname: Martin T orcidid: 0000-0001-7191-9768 surname: Zanni fullname: Zanni, Martin T organization: Department of Chemistry |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32598850$$D View this record in MEDLINE/PubMed |
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