Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials.

ABSTRACT: There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial

SUBMITTER: Lansford JL 

PROVIDER: S-EPMC7089992 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature