ArticleFaraday discussions2024
First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects.
Article in Faraday discussions, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Simultaneous learning of static and dynamic charges.Physical chemistry chemical physics : PCCP · 2026Article
- Predictive Quantum Vibrational Spectra through Active Learning 4G-NNPs.The journal of physical chemistry letters · 2026Article
- Machine-Learning Ice Spectra: From 1 to 256 Features.Journal of chemical theory and computation · 2026Article
- Quantum vibrational spectroscopy with classical trajectories.Chemical science · 2026Review
- Tracking and Controlling Monolayer Water in Gold Nanogaps using Extreme Plasmonic Spectroscopy.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- COProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Unified differentiable learning of electric response.Nature communications · 2025Article
- Interpolating numerically exact many-body wave functions for accelerated molecular dynamics.Nature communications · 2025Article
- Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies.Faraday discussions · 2025Article
- Electric-field driven nuclear dynamics of liquids and solids from a multi-valued machine-learned dipolar model.npj computational materials · 2025Article
- Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction.npj computational materials · 2025Article
- Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials.Journal of chemical theory and computation · 2024Article
- Quasi-one-dimensional hydrogen bonding in nanoconfined ice.Nature communications · 2024Article
- Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra.Journal of chemical theory and computation · 2024Article
- Fast Quasi-Centroid Molecular Dynamics for Water and Ice.The journal of physical chemistry. B · 2023Article
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Authors and funding
4 authors.
Funding
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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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