Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts.
Vinson LiaoMaximilian CohenYifan WangDionisios G VlachosPublished in: Nature communications (2023)
Infrared (IR) spectra of adsorbate vibrational modes are sensitive to adsorbate/metal interactions, accurate, and easily obtainable in-situ or operando. While they are the gold standards for characterizing single-crystals and large nanoparticles, analogous spectra for highly dispersed heterogeneous catalysts consisting of single-atoms and ultra-small clusters are lacking. Here, we combine data-based approaches with physics-driven surrogate models to generate synthetic IR spectra from first-principles. We bypass the vast combinatorial space of clusters by determining viable, low-energy structures using machine-learned Hamiltonians, genetic algorithm optimization, and grand canonical Monte Carlo calculations. We obtain first-principles vibrations on this tractable ensemble and generate single-cluster primary spectra analogous to pure component gas-phase IR spectra. With such spectra as standards, we predict cluster size distributions from computational and experimental data, demonstrated in the case of CO adsorption on Pd/CeO 2 (111) catalysts, and quantify uncertainty using Bayesian Inference. We discuss extensions for characterizing complex materials towards closing the materials gap.
Keyphrases
- density functional theory
- monte carlo
- molecular dynamics
- highly efficient
- high resolution
- molecular dynamics simulations
- deep learning
- machine learning
- electronic health record
- metal organic framework
- copy number
- transition metal
- genome wide
- gene expression
- silver nanoparticles
- ionic liquid
- artificial intelligence
- data analysis
- neural network
- raman spectroscopy