Quantitatively Determining Surface–Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning

Learning microscopic properties of a material from its
macroscopic
measurables is a grand and challenging goal in physical science. Conventional
wisdom is to first identify material structures exploiting characterization
tools, such as spectroscopy, and then to infer properties of interest,
often with assistance of theory and simulations. This indirect approach
has limitations due to the accumulation of errors from retrieving
structures from spectral signals and the lack of quantitative structure–property
relationship. A new pathway directly from spectral signals to microscopic
properties is highly desirable, as it would offer valuable guidance
toward materials evaluation and design via spectroscopic measurements.
Herein, we exploit machine-learned vibrational spectroscopy to establish
quantitative spectrum–property relationships. Key interaction
properties of substrate–adsorbate systems, including adsorption
energy and charge transfer, are quantitatively determined directly
from Infrared and Raman spectroscopic signals of the adsorbates. The
machine-learned spectrum–property relationships are presented
as mathematical formulas, which are physically interpretable and therefore
transferrable to a series of metal/alloy surfaces. The demonstrated
ability of quantitative determination of hard-to-measure microscopic
properties using machine-learned spectroscopy will significantly broaden
the applicability of conventional spectroscopic techniques for materials
design and high throughput screening under operando conditions.