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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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Kapil V, Kovács DP, Csányi G, Michaelides A. First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects. Faraday Discuss 2024; 249:50-68. [PMID: 37799072 PMCID: PMC10845015 DOI: 10.1039/d3fd00113j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/18/2023] [Indexed: 10/07/2023]
Abstract
Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena. However, extracting structural and dynamical information from vibrational spectra is a challenge that requires first-principles simulations, including non-Condon and quantum nuclear effects. We address this challenge by developing a machine-learning enhanced first-principles framework to speed up predictive modelling of infrared, Raman, and sum-frequency generation spectra. Our approach uses machine learning potentials that encode quantum nuclear effects to generate quantum trajectories using simple molecular dynamics efficiently. In addition, we reformulate bulk and interfacial selection rules to express them unambiguously in terms of the derivatives of polarisation and polarisabilities of the whole system and predict these derivatives efficiently using fully-differentiable machine learning models of dielectric response tensors. We demonstrate our framework's performance by predicting the IR, Raman, and sum-frequency generation spectra of liquid water, ice and the water-air interface by achieving near quantitative agreement with experiments at nearly the same computational efficiency as pure classical methods. Finally, to aid the experimental discovery of new phases of nanoconfined water, we predict the temperature-dependent vibrational spectra of monolayer water across the solid-hexatic-liquid phases transition.
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Affiliation(s)
- Venkat Kapil
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
| | | | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, UK
| | - Angelos Michaelides
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
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3
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Matin S, Allen AEA, Smith J, Lubbers N, Jadrich RB, Messerly R, Nebgen B, Li YW, Tretiak S, Barros K. Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment. J Chem Theory Comput 2024. [PMID: 38307009 DOI: 10.1021/acs.jctc.3c01051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2024]
Abstract
Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. We investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.
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Affiliation(s)
- Sakib Matin
- Department of Physics, Boston University, Boston, Massachusetts 02215, United States
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Alice E A Allen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Justin Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- NVIDIA Corp., Santa Clara, California 95051, United States
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ryan B Jadrich
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Richard Messerly
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Ying Wai Li
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
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4
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Zhang Y, Jiang B. Universal machine learning for the response of atomistic systems to external fields. Nat Commun 2023; 14:6424. [PMID: 37827998 PMCID: PMC10570356 DOI: 10.1038/s41467-023-42148-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 10/01/2023] [Indexed: 10/14/2023] Open
Abstract
Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a universal field-induced recursively embedded atom neural network (FIREANN) model, which integrates a pseudo field vector-dependent feature into atomic descriptors to represent system-field interactions with rigorous rotational equivariance. This "all-in-one" approach correlates various response properties like dipole moment and polarizability with the field-dependent potential energy in a single model, very suitable for spectroscopic and dynamics simulations in molecular and periodic systems in the presence of electric fields. Especially for periodic systems, we find that FIREANN can overcome the intrinsic multiple-value issue of the polarization by training atomic forces only. These results validate the universality and capability of the FIREANN method for efficient first-principles modeling of complicated systems in strong external fields.
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Affiliation(s)
- Yaolong Zhang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230026, China
- École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland
| | - Bin Jiang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230026, China.
- Hefei National Laboratory, University of Science and Technology of China, Hefei, 230088, China.
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5
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Tang Z, Bromley ST, Hammer B. A machine learning potential for simulating infrared spectra of nanosilicate clusters. J Chem Phys 2023; 158:2895243. [PMID: 37290080 DOI: 10.1063/5.0150379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/23/2023] [Indexed: 06/10/2023] Open
Abstract
The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here, we apply an accurate and efficient protocol to collect training data for constructing a neural network-based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium and in circumstellar environments.
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Affiliation(s)
- Zeyuan Tang
- Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, Aarhus C 8000, Denmark
| | - Stefan T Bromley
- Departament de Ciència de Materials i Química Física and Institut de Química Teòrica i Computatcional (IQTCUB), Universitat de Barcelona, c/Martí i Franquès 1-11, 08028 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
| | - Bjørk Hammer
- Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, Aarhus C 8000, Denmark
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6
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Botella R, Kistanov AA. A Unified View of Vibrational Spectroscopy Simulation through Kernel Density Estimations. J Phys Chem Lett 2023; 14:3691-3697. [PMID: 37037010 PMCID: PMC10123815 DOI: 10.1021/acs.jpclett.3c00665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
To date, vibrational simulation results constitute more of an experimental support than a predictive tool, as the simulated vibrational modes are discrete due to quantization. This is different from what is obtained experimentally. Here, we propose a way to combine outputs such as the phonon density of states surrogate and peak intensities obtained from ab initio simulations to allow comparison with experimental data by using machine learning. This work is paving the way for using simulated vibrational spectra as a tool to identify materials with defined stoichiometry, enabling the separation of genuine vibrational features of pure phases from morphological and defect-induced signals.
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7
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Mendive-Tapia D, Meyer HD, Vendrell O. Optimal Mode Combination in the Multiconfiguration Time-Dependent Hartree Method through Multivariate Statistics: Factor Analysis and Hierarchical Clustering. J Chem Theory Comput 2023; 19:1144-1156. [PMID: 36716214 DOI: 10.1021/acs.jctc.2c01089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The multiconfiguration time-dependent Hartree (MCTDH) method and its multilayer extension (ML-MCTDH) are powerful algorithms for the efficient computation of nuclear quantum dynamics in high-dimensional systems. By providing time-dependent variational orbitals and an optimal choice of layered effective degrees of freedom, one is able to reduce the computational cost to an amenable number of configurations. However, choices related to selecting properly the mode grouping and tensor tree are strongly system dependent and, thus far, subjectively based on intuition and/or experience. Therefore, herein we detail a new protocol based on multivariate statistics─more specifically, factor analysis and hierarchical clustering─for a reliable and convenient guiding in the optimal design of such complex "system-of-systems" tensor-network decompositions. The advantages of employing the new algorithm and its applicability are tested on water and two floppy protonated water clusters with large amplitude motions.
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Affiliation(s)
- David Mendive-Tapia
- Theoretische Chemie, Universität Heidelberg, Im Neuenheimer Feld 229, D-69120Heidelberg, Germany
| | - Hans-Dieter Meyer
- Theoretische Chemie, Universität Heidelberg, Im Neuenheimer Feld 229, D-69120Heidelberg, Germany
| | - Oriol Vendrell
- Theoretische Chemie, Universität Heidelberg, Im Neuenheimer Feld 229, D-69120Heidelberg, Germany
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8
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Schienbein P. Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor. J Chem Theory Comput 2023; 19:705-712. [PMID: 36695707 PMCID: PMC9933433 DOI: 10.1021/acs.jctc.2c00788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Vibrational spectroscopy is a key technique to elucidate microscopic structure and dynamics. Without the aid of theoretical approaches, it is, however, often difficult to understand such spectra at a microscopic level. Ab initio molecular dynamics has repeatedly proved to be suitable for this purpose; however, the computational cost can be daunting. Here, the E(3)-equivariant neural network e3nn is used to fit the atomic polar tensor of liquid water a posteriori on top of existing molecular dynamics simulations. Notably, the introduced methodology is general and thus transferable to any other system as well. The target property is most fundamental and gives access to the IR spectrum, and more importantly, it is a highly powerful tool to directly assign IR spectral features to nuclear motion─a connection which has been pursued in the past but only using severe approximations due to the prohibitive computational cost. The herein introduced methodology overcomes this bottleneck. To benchmark the machine learning model, the IR spectrum of liquid water is calculated, indeed showing excellent agreement with the explicit reference calculation. In conclusion, the presented methodology gives a new route to calculate accurate IR spectra from molecular dynamics simulations and will facilitate the understanding of such spectra on a microscopic level.
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9
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Moura RT, Quintano M, Antonio JJ, Freindorf M, Kraka E. Automatic Generation of Local Vibrational Mode Parameters: From Small to Large Molecules and QM/MM Systems. J Phys Chem A 2022; 126:9313-9331. [DOI: 10.1021/acs.jpca.2c07871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Renaldo T. Moura
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas75275-0314, United States
- Department of Chemistry and Physics Center of Agrarian Sciences, Federal University of Paraiba, Areia, PB58397-000, Brazil
| | - Mateus Quintano
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas75275-0314, United States
| | - Juliana J. Antonio
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas75275-0314, United States
| | - Marek Freindorf
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas75275-0314, United States
| | - Elfi Kraka
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas75275-0314, United States
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10
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Kraka E, Quintano M, La Force HW, Antonio JJ, Freindorf M. The Local Vibrational Mode Theory and Its Place in the Vibrational Spectroscopy Arena. J Phys Chem A 2022; 126:8781-8798. [DOI: 10.1021/acs.jpca.2c05962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Elfi Kraka
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Mateus Quintano
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Hunter W. La Force
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Juliana J. Antonio
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Marek Freindorf
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
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11
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Larsson HR, Schröder M, Beckmann R, Brieuc F, Schran C, Marx D, Vendrell O. State-resolved infrared spectrum of the protonated water dimer: revisiting the characteristic proton transfer doublet peak. Chem Sci 2022; 13:11119-11125. [PMID: 36320484 PMCID: PMC9517273 DOI: 10.1039/d2sc03189b] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/26/2022] [Indexed: 11/29/2023] Open
Abstract
The infrared (IR) spectra of protonated water clusters encode precise information on the dynamics and structure of the hydrated proton. However, the strong anharmonic coupling and quantum effects of these elusive species remain puzzling up to the present day. Here, we report unequivocal evidence that the interplay between the proton transfer and the water wagging motions in the protonated water dimer (Zundel ion) giving rise to the characteristic doublet peak is both more complex and more sensitive to subtle energetic changes than previously thought. In particular, hitherto overlooked low-intensity satellite peaks in the experimental spectrum are now unveiled and mechanistically assigned. Our findings rely on the comparison of IR spectra obtained using two highly accurate potential energy surfaces in conjunction with highly accurate state-resolved quantum simulations. We demonstrate that these high-accuracy simulations are important for providing definite assignments of the complex IR signals of fluxional molecules.
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Affiliation(s)
- Henrik R Larsson
- Department of Chemistry and Biochemistry, University of California Merced CA 95343 USA
- Division of Chemistry and Chemical Engineering, California Institute of Technology Pasadena CA 91125 USA
| | - Markus Schröder
- Theoretische Chemie, Physikalisch-Chemisches Institut, Universität Heidelberg Im Neuenheimer Feld 229 D - 69120 Heidelberg Germany
| | - Richard Beckmann
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum 44780 Bochum Germany
| | - Fabien Brieuc
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum 44780 Bochum Germany
| | - Christoph Schran
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum 44780 Bochum Germany
| | - Dominik Marx
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum 44780 Bochum Germany
| | - Oriol Vendrell
- Theoretische Chemie, Physikalisch-Chemisches Institut, Universität Heidelberg Im Neuenheimer Feld 229 D - 69120 Heidelberg Germany
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