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Yang J, Cong Y, Li Y, Li H. Machine Learning Approach Based on a Range-Corrected Deep Potential Model for Efficient Vibrational Frequency Computation. J Chem Theory Comput 2023; 19:6366-6374. [PMID: 37652890 DOI: 10.1021/acs.jctc.3c00386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
As an ensemble average result, vibrational spectrum simulation can be time-consuming with high accuracy methods. We present a machine learning approach based on the range-corrected deep potential (DPRc) model to improve the computing efficiency. The DPRc method divides the system into "probe region" and "solvent region"; "solvent-solvent" interactions are not counted in the neural network. We applied the approach to two systems: formic acid C═O stretching and MeCN C≡N stretching vibrational frequency shifts in water. All data sets were prepared using the quantum vibration perturbation approach. Effects of different region divisions, one-body correction, cut range, and training data size were tested. The model with a single-molecule "probe region" showed stable accuracy; it ran roughly 10 times faster than regular deep potential and reduced the training time by about four. The approach is efficient, easy to apply, and extendable to calculating various spectra.
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Affiliation(s)
- Jitai Yang
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, P. R. China
| | - Yang Cong
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, P. R. China
| | - You Li
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, P. R. China
| | - Hui Li
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, P. R. China
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Kwac K, Freedman H, Cho M. Machine Learning Approach for Describing Water OH Stretch Vibrations. J Chem Theory Comput 2021; 17:6353-6365. [PMID: 34498885 DOI: 10.1021/acs.jctc.1c00540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A machine learning approach employing neural networks is developed to calculate the vibrational frequency shifts and transition dipole moments of the symmetric and antisymmetric OH stretch vibrations of a water molecule surrounded by water molecules. We employed the atom-centered symmetry functions (ACSFs), polynomial functions, and Gaussian-type orbital-based density vectors as descriptor functions and compared their performances in predicting vibrational frequency shifts using the trained neural networks. The ACSFs perform best in modeling the frequency shifts of the OH stretch vibration of water among the types of descriptor functions considered in this paper. However, the differences in performance among these three descriptors are not significant. We also tried a feature selection method called CUR matrix decomposition to assess the importance and leverage of the individual functions in the set of selected descriptor functions. We found that a significant number of those functions included in the set of descriptor functions give redundant information in describing the configuration of the water system. We here show that the predicted vibrational frequency shifts by trained neural networks successfully describe the solvent-solute interaction-induced fluctuations of OH stretch frequencies.
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Affiliation(s)
- Kijeong Kwac
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science (IBS), Seoul 02841, Republic of Korea
| | - Holly Freedman
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science (IBS), Seoul 02841, Republic of Korea
| | - Minhaeng Cho
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science (IBS), Seoul 02841, Republic of Korea.,Department of Chemistry, Korea University, Seoul 02841, Republic of Korea
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Baiz CR, Błasiak B, Bredenbeck J, Cho M, Choi JH, Corcelli SA, Dijkstra AG, Feng CJ, Garrett-Roe S, Ge NH, Hanson-Heine MWD, Hirst JD, Jansen TLC, Kwac K, Kubarych KJ, Londergan CH, Maekawa H, Reppert M, Saito S, Roy S, Skinner JL, Stock G, Straub JE, Thielges MC, Tominaga K, Tokmakoff A, Torii H, Wang L, Webb LJ, Zanni MT. Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction. Chem Rev 2020; 120:7152-7218. [PMID: 32598850 PMCID: PMC7710120 DOI: 10.1021/acs.chemrev.9b00813] [Citation(s) in RCA: 165] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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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Affiliation(s)
- Carlos R. Baiz
- Department of Chemistry, University of Texas at Austin, Austin, TX 78712, U.S.A
| | - Bartosz Błasiak
- Department of Physical and Quantum Chemistry, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
| | - Jens Bredenbeck
- Johann Wolfgang Goethe-University, Institute of Biophysics, Max-von-Laue-Str. 1, 60438, Frankfurt am Main, Germany
| | - Minhaeng Cho
- Center for Molecular Spectroscopy and Dynamics, Seoul 02841, Republic of Korea
- Department of Chemistry, Korea University, Seoul 02841, Republic of Korea
| | - Jun-Ho Choi
- Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Steven A. Corcelli
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, U.S.A
| | - Arend G. Dijkstra
- School of Chemistry and School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, U.K
| | - Chi-Jui Feng
- Department of Chemistry, James Franck Institute and Institute for Biophysical Dynamics, University of Chicago, Chicago, IL 60637, U.S.A
| | - Sean Garrett-Roe
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15260, U.S.A
| | - Nien-Hui Ge
- Department of Chemistry, University of California at Irvine, Irvine, CA 92697-2025, U.S.A
| | - Magnus W. D. Hanson-Heine
- School of Chemistry, University of Nottingham, Nottingham, University Park, Nottingham, NG7 2RD, U.K
| | - Jonathan D. Hirst
- School of Chemistry, University of Nottingham, Nottingham, University Park, Nottingham, NG7 2RD, U.K
| | - Thomas L. C. Jansen
- University of Groningen, Zernike Institute for Advanced Materials, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Kijeong Kwac
- Center for Molecular Spectroscopy and Dynamics, Seoul 02841, Republic of Korea
| | - Kevin J. Kubarych
- Department of Chemistry, University of Michigan, 930 N. University Ave., Ann Arbor, MI 48109, U.S.A
| | - Casey H. Londergan
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, U.S.A
| | - Hiroaki Maekawa
- Department of Chemistry, University of California at Irvine, Irvine, CA 92697-2025, U.S.A
| | - Mike Reppert
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Shinji Saito
- Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, Myodaiji, Okazaki, 444-8585, Japan
| | - Santanu Roy
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6110, U.S.A
| | - James L. Skinner
- Institute for Molecular Engineering, University of Chicago, Chicago, IL 60637, U.S.A
| | - Gerhard Stock
- Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany
| | - John E. Straub
- Department of Chemistry, Boston University, Boston, MA 02215, U.S.A
| | - Megan C. Thielges
- Department of Chemistry, Indiana University, 800 East Kirkwood, Bloomington, Indiana 47405, U.S.A
| | - Keisuke Tominaga
- Molecular Photoscience Research Center, Kobe University, Nada, Kobe 657-0013, Japan
| | - Andrei Tokmakoff
- Department of Chemistry, James Franck Institute and Institute for Biophysical Dynamics, University of Chicago, Chicago, IL 60637, U.S.A
| | - Hajime Torii
- 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
| | - Lu Wang
- Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, U.S.A
| | - Lauren J. Webb
- Department of Chemistry, The University of Texas at Austin, 105 E. 24th Street, STOP A5300, Austin, Texas 78712, U.S.A
| | - Martin T. Zanni
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706-1396, U.S.A
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