First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects

Kapil, Venkat; Kovács, Dávid Péter; Csänyi, Gábor; Michaelides, Angelos

doi:10.1039/d3fd00113j

Cited by 17 publications

(5 citation statements)

References 104 publications

(196 reference statements)

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“…For the q-TIP4P/f water model studied here, they lead to an anharmonic red shift of around 100 cm –1 in the intramolecular O–H stretching band and a smaller red shift in the intramolecular H–O–H bending band but very little change in the line shape of either band (see Figures and ). Recent Te PIGS calculations demonstrate that this remains largely true for calculations using ab initio DFT potential energy surfaces, albeit with larger changes to the intensities and the line shapes of the bands in ice …”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Recent Te PIGS calculations demonstrate that this remains largely true for calculations using ab initio DFT potential energy surfaces, albeit with larger changes to the intensities and the line shapes of the bands in ice. 36 There will be many situations in which it is reasonable to ignore the relatively small quantum effects that we are discussing here and use classical molecular dynamics to simulate the vibrational spectrum. However, there will be other situations where the goal is to understand anharmonic red and blue shifts in vibrational spectra and how they change with temperature and isotopic substitution, which are questions that classical molecular dynamics is incapable of answering.…”

Section: ■ Concluding Remarksmentioning

confidence: 99%

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Fast Quasi-Centroid Molecular Dynamics for Water and Ice

Lawrence,

Lieberherr,

Fletcher

et al. 2023

J. Phys. Chem. B

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We describe how the fast quasi-centroid molecular dynamics (f-QCMD) method can be applied to condensed-phase systems by approximating the quasi-centroid potential of mean force as a sum of inter- and intramolecular corrections to the classical interaction potential. The corrections are found by using a regularized iterative Boltzmann inversion procedure to recover the inter- and intramolecular quasi-centroid distribution functions obtained from a path integral molecular dynamics simulation. The resulting methodology is found to give good agreement with a previously published QCMD dipole absorption spectrum for liquid water and satisfactory agreement for ice. It also gives good agreement with spectra from a recent implementation of CMD that uses a precomputed elevated temperature potential of mean force. Modern centroid molecular dynamics methods, therefore, appear to be reaching a consensus regarding the impact of nuclear quantum effects on the vibrational spectra of water and ice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: ■ Concluding Remarksmentioning

confidence: 99%

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Fast Quasi-Centroid Molecular Dynamics for Water and Ice

Lawrence,

Lieberherr,

Fletcher

et al. 2023

J. Phys. Chem. B

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“…We note that quantum effects can be actually rather pronounced in water as has been shown by path integral MD simulations in, e.g., refs. 65 − 67 Here, however, we decided to carry out classical MD simulations in order to enable a one-to-one comparison with the results of earlier studies.…”

Section: Applicationsmentioning

confidence: 99%

Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra

Xu,

Rosander,

Schäfer

et al. 2024

J. Chem. Theory Comput.

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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 (BaZrO 3 ). 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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“…The MACE approach extends the atomic cluster expansion (ACE) method [122] and achieves encoding of high-order many-body information of the nuclear structure in a computationally efficient manner. This approach has been applied to inter-atomic potentials [123], and recently to the modelling of infrared, Raman, and sum-frequency generation spectra [124]. It has not yet been used to simulate x-ray spectroscopic observables.…”

Section: Molecular Graph Representationsmentioning

confidence: 99%

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

Penfold,

Watson,

Middleton

et al. 2024

Mach. Learn.: Sci. Technol.

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Computational spectroscopy has emerged as a critical tool for researchers looking to achieve both qualitative and quantitative interpretations of experimental spectra. Over the past decade, increased interactions between experiment and theory have created a positive feedback loop that has stimulated developments in both domains. In particular, the increased accuracy of calculations has led to them becoming an indispensable tool for the analysis of spectroscopies across the electromagnetic spectrum. This progress is especially well demonstrated for short-wavelength techniques, e.g. core-hole (X-ray) spectroscopies, whose prevalence has increased following the advent of modern X-ray facilities including third-generation synchrotrons and X-ray free-electron lasers (XFELs). While calculations based on well-established wavefunction or density-functional methods continue to dominate the greater part of spectral analyses in the literature, emerging developments in machine-learning algorithms are beginning to open up new opportunities to complement these traditional techniques with fast, accurate, and affordable 'black-box' approaches. This Topical Review recounts recent progress in data-driven/machine-learning approaches for computational X-ray spectroscopy. We discuss the achievements and limitations of the presently-available approaches and review the potential that these techniques have to expand the scope and reach of computational and experimental X-ray spectroscopic studies.

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First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects

Abstract: Vibrational spectroscopy is critical to understanding the structure and dynamics of interfacial water, which plays a key role in natural and technologically-relevant phenomena. However, interpreting the vibrational spectra of complex...

Cited by 17 publications

References 104 publications

Fast Quasi-Centroid Molecular Dynamics for Water and Ice

Fast Quasi-Centroid Molecular Dynamics for Water and Ice

Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

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