Fully First-Principles Surface Spectroscopy with Machine Learning.

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 ful

SUBMITTER: Litman Y 

PROVIDER: S-EPMC10510433 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature