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Xu N, Rosander P, Schäfer C, Lindgren E, Österbacka N, Fang M, Chen W, He Y, Fan Z, Erhart P. Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra. J Chem Theory Comput 2024; 20:3273-3284. [PMID: 38572734 PMCID: PMC11044275 DOI: 10.1021/acs.jctc.3c01343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/19/2024] [Accepted: 04/01/2024] [Indexed: 04/05/2024]
Abstract
Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning, in particular, the dynamics of these systems. Atomic scale simulations can be used to predict such spectra but are often severely limited due to high computational cost or the need for strong approximations that limit the application range and reliability. Here, we introduce a machine learning (ML) accelerated approach that addresses these shortcomings and provides a significant performance boost in terms of data and computational efficiency compared with earlier ML schemes. To this end, we generalize the neuroevolution potential approach to enable the prediction of rank one and two tensors to obtain the tensorial neuroevolution potential (TNEP) scheme. We apply the resulting framework to construct models for the dipole moment, polarizability, and susceptibility of molecules, liquids, and solids and show that our approach compares favorably with several ML models from the literature with respect to accuracy and computational efficiency. Finally, we demonstrate the application of the TNEP approach to the prediction of infrared and Raman spectra of liquid water, a molecule (PTAF-), and a prototypical perovskite with strong anharmonicity (BaZrO3). The TNEP approach is implemented in the free and open source software package gpumd, which makes this methodology readily available to the scientific community.
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Affiliation(s)
- Nan Xu
- Institute
of Zhejiang University-Quzhou, Quzhou 324000, P. R. China
- College
of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, P. R. China
| | - Petter Rosander
- Department
of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
| | - Christian Schäfer
- Department
of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
| | - Eric Lindgren
- Department
of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
| | - Nicklas Österbacka
- Department
of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
| | - Mandi Fang
- Institute
of Zhejiang University-Quzhou, Quzhou 324000, P. R. China
- College
of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, P. R. China
| | - Wei Chen
- State
Key Laboratory of Multiphase Complex Systems, Institute of Process
Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Yi He
- Institute
of Zhejiang University-Quzhou, Quzhou 324000, P. R. China
- College
of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, P. R. China
- Department
of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Zheyong Fan
- College
of Physical Science and Technology, Bohai
University, Jinzhou 121013, P. R. China
| | - Paul Erhart
- Department
of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
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2
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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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3
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Inoue K, Litman Y, Wilkins DM, Nagata Y, Okuno M. Is Unified Understanding of Vibrational Coupling of Water Possible? Hyper-Raman Measurement and Machine Learning Spectra. J Phys Chem Lett 2023; 14:3063-3068. [PMID: 36947156 DOI: 10.1021/acs.jpclett.3c00398] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The impact of the vibrational coupling of the OH stretch mode on the spectra differs significantly between IR and Raman spectra of water. Unified understanding of the vibrational couplings is not yet achieved. By using a different class of vibrational spectroscopy, hyper-Raman (HR) spectroscopy, together with machine-learning-assisted HR spectra calculation, we examine the impact of the vibrational couplings of water through the comparison of isotopically diluted H2O and pure H2O. We found that the isotopic dilution reduces the HR bandwidths, but the impact of the vibrational coupling is smaller than in the IR and parallel-polarized Raman. Machine learning HR spectra indicate that the intermolecular coupling plays a major role in broadening the bandwidth, while the intramolecular coupling is negligibly small, which is consistent with the IR and Raman spectra. Our result clearly demonstrates a limited impact of the intramolecular vibration, independent of the selection rules of vibrational spectroscopies.
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Affiliation(s)
- Kazuki Inoue
- Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan
| | - Yair Litman
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - David M Wilkins
- Atomistic Simulation Centre, School of Mathematics and Physics, Queen's University Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom
| | - Yuki Nagata
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Masanari Okuno
- Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan
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4
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Käser S, Vazquez-Salazar LI, Meuwly M, Töpfer K. Neural network potentials for chemistry: concepts, applications and prospects. DIGITAL DISCOVERY 2023; 2:28-58. [PMID: 36798879 PMCID: PMC9923808 DOI: 10.1039/d2dd00102k] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on a larger scale.
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Affiliation(s)
- Silvan Käser
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
| | | | - Markus Meuwly
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
| | - Kai Töpfer
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
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Han R, Ketkaew R, Luber S. A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra. J Phys Chem A 2022; 126:801-812. [PMID: 35133168 DOI: 10.1021/acs.jpca.1c10417] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Machine learning has become more and more popular in computational chemistry, as well as in the important field of spectroscopy. In this concise review, we walk the reader through a short summary of machine learning algorithms and a comprehensive discussion on the connection between machine learning methods and vibrational spectroscopy, particularly for the case of infrared and Raman spectroscopy. We also briefly discuss state-of-the-art molecular representations which serve as meaningful inputs for machine learning to predict vibrational spectra. In addition, this review provides an overview of the transferability and best practices of machine learning in the prediction of vibrational spectra as well as possible future research directions.
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Affiliation(s)
- Ruocheng Han
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Rangsiman Ketkaew
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Sandra Luber
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
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Das B, Chandra A. Effects of Stearyl Alcohol Monolayer on the Structure, Dynamics and Vibrational Sum Frequency Generation Spectroscopy of Interfacial Water. Phys Chem Chem Phys 2022; 24:7374-7386. [DOI: 10.1039/d1cp04944e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The structure, dynamics and vibrational spectroscopy of water surface covered by a monolayer of stearyl alcohol (STA) are investigated by means of molecular dynamics simulations and vibrational sum frequency generation...
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