ArticleThe journal of physical chemistry letters2023
Fully First-Principles Surface Spectroscopy with Machine Learning.
Article in The journal of physical chemistry letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Learning Electronic Polarization in Molecular Systems: Vibrational Spectroscopy of Ethanol-Water Mixtures.Journal of chemical information and modeling · 2026Article
- Breaking the Air-Water Paradigm: Ion Behavior at Hydrophobic Solid-Water Interfaces.Journal of the American Chemical Society · 2026Article
- Molecular Fingerprints of Ice Surfaces in Sum Frequency Generation Spectra: A First-Principles Machine Learning Study.JACS Au · 2025Article
- Electric-field driven nuclear dynamics of liquids and solids from a multi-valued machine-learned dipolar model.npj computational materials · 2025Article
- Robust Computation and Analysis of Vibrational Spectra of Layered Framework Materials Including Host-Guest Interactions.Journal of chemical theory and computation · 2024Article
- Learning Electronic Polarizations in Aqueous Systems.Journal of chemical information and modeling · 2024Article
- First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects.Faraday discussions · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially due to the continuous development of surface-specific spectroscopies, such as vibrational sum-frequency generation (VSFG). As in other vibrational spectroscopies, we must turn to atomistic simulations to extract all of the information encoded in the VSFG spectra. The high computational cost associated with existing methods means that they have limitations in representing systems with complex electronic structure or in achieving statistical convergence. In this work, we combine high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression to overcome these constraints. We show that it is possible to model VSFG signals with fully
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Registered trials
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