TY - JOUR
T1 - Interpretable machine learned predictions of adsorption energies at the metal-oxide interface
AU - Nielsen, Marius Juul
AU - Kempen, Luuk H.E.
AU - de Neergaard Ravn, Julie
AU - Cheula, Raffaele
AU - Andersen, Mie
N1 - Publisher Copyright:
© 2025 Author(s).
PY - 2025/7/28
Y1 - 2025/7/28
N2 - The conversion of CO2 to value-added compounds is an important part of the effort to store and reuse atmospheric CO2 emissions. Here, we focus on CO2 hydrogenation over so-called inverse catalysts: transition metal oxide clusters supported on metal surfaces. The conventional approach for computational screening of such candidate catalyst materials involves a reliance on density functional theory (DFT) to obtain accurate adsorption energies at a significant computational cost. Here, we present a machine learning (ML)-accelerated workflow for obtaining adsorption energies at the metal-oxide interface. We enumerate possible binding sites at the clusters and use DFT to sample a subset of these with diverse local adsorbate environments. The dataset is used to explore interpretable and black-box ML models with the aim of revealing the electronic and structural factors controlling adsorption at metal-oxide interfaces. Furthermore, the explored ML models can be used for low-cost prediction of adsorption energies on structures outside of the original training dataset. The workflow presented here, along with the insights into trends in adsorption energies at metal-oxide interfaces, will be useful for identifying active sites, predicting parameters required for microkinetic modeling of reactions on complex catalyst materials, and accelerating data-driven catalyst design.
AB - The conversion of CO2 to value-added compounds is an important part of the effort to store and reuse atmospheric CO2 emissions. Here, we focus on CO2 hydrogenation over so-called inverse catalysts: transition metal oxide clusters supported on metal surfaces. The conventional approach for computational screening of such candidate catalyst materials involves a reliance on density functional theory (DFT) to obtain accurate adsorption energies at a significant computational cost. Here, we present a machine learning (ML)-accelerated workflow for obtaining adsorption energies at the metal-oxide interface. We enumerate possible binding sites at the clusters and use DFT to sample a subset of these with diverse local adsorbate environments. The dataset is used to explore interpretable and black-box ML models with the aim of revealing the electronic and structural factors controlling adsorption at metal-oxide interfaces. Furthermore, the explored ML models can be used for low-cost prediction of adsorption energies on structures outside of the original training dataset. The workflow presented here, along with the insights into trends in adsorption energies at metal-oxide interfaces, will be useful for identifying active sites, predicting parameters required for microkinetic modeling of reactions on complex catalyst materials, and accelerating data-driven catalyst design.
UR - https://www.scopus.com/pages/publications/105012137868
U2 - 10.1063/5.0282674
DO - 10.1063/5.0282674
M3 - Journal article
C2 - 40736056
AN - SCOPUS:105012137868
SN - 0021-9606
VL - 163
JO - Journal of Chemical Physics
JF - Journal of Chemical Physics
IS - 4
M1 - 044708
ER -