Fully first-principles surface spectroscopy with machine learning

Yair Litman*, Jinggang Lan, Yuki Nagata, David M. Wilkins*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
2 Downloads (Pure)

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 ab initio accuracy using machine learning and illustrate the versatility of our approach on the water/air interface. Our strategy allows us to identify the main sources of theoretical inaccuracy and establish a clear pathway toward the modeling of surface-sensitive spectroscopy of complex interfaces.

Original languageEnglish
Pages (from-to)8175-8182
Number of pages8
JournalJournal of Physical Chemistry Letters
Volume14
Issue number36
Early online date06 Sept 2023
DOIs
Publication statusPublished - 14 Sept 2023

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