Selectivity- and Activity-Aware Catalyst Descriptors for CO2 Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
Abstract
Adsorption energy distributions (AEDs) have emerged as a powerful and increasingly adopted descriptor for catalytic performance in high-entropy alloys and, more recently, in conventional metallic alloy nanocrystal catalysts. By accounting for diverse adsorption sites and crystallographic facets, AEDs more fully represent nanoparticle-based catalytic surfaces and show strong promise for accelerating rational design and discovery of heterogeneous catalysts, especially for CO2 hydrogenation. However, previous high-throughput screenings have not provided simultaneous facet-level insight into catalytic activity, product selectivity, and thermodynamic stability. Here, we implement and extend the AED framework to individual crystallographic facets, combining facet-resolved adsorption fingerprints with Wulff-derived facet abundances and an interpretable latent-space analysis of C1-product selectivity. We employ universal machine-learned force fields trained on Open Catalyst Project data to compute adsorption energies across 226 experimentally observed metals, binary alloys, and previously unexplored ternary alloys, encompassing ~1.4 million adsorption configurations on >2,600 crystallographically distinct surfaces. Using distribution-based similarity analysis and principal component analysis of AED moments, we identify composition-facet combinations with adsorption landscapes associated with activity and qualitative selectivity trends toward methanol, methane, formic acid, and CO. Our framework links surface structure to adsorption-based catalytic trends, providing experimentally testable composition-facet candidates for further investigation and validation.
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BibTeX
@misc{pisal2026selectivity,
title = {{Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields}},
author = {Prajwal Pisal and Ondřej Krejčí and Patrick Rinke},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.07714},
eprint = {2605.07714},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.07714},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Selectivity- and Activity-Aware Catalyst Descriptors for CO_2 Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
AU - Prajwal Pisal
AU - Ondřej Krejčí
AU - Patrick Rinke
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2605.07714
UR - https://arxiv.org/abs/2605.07714
AB - Adsorption energy distributions (AEDs) have emerged as a powerful and increasingly adopted descriptor for catalytic performance in high-entropy alloys and, more recently, in conventional metallic alloy nanocrystal catalysts. By accounting for diverse adsorption sites and crystallographic facets, AEDs more fully represent nanoparticle-based catalytic surfaces and show strong promise for accelerating rational design and discovery of heterogeneous catalysts, especially for CO_2 hydrogenation. However, previous high-throughput screenings have not provided simultaneous facet-level insight into catalytic activity, product selectivity, and thermodynamic stability. Here, we implement and extend the AED framework to individual crystallographic facets, combining facet-resolved adsorption fingerprints with Wulff-derived facet abundances and an interpretable latent-space analysis of C1-product selectivity. We employ universal machine-learned force fields trained on Open Catalyst Project data to compute adsorption energies across 226 experimentally observed metals, binary alloys, and previously unexplored ternary alloys, encompassing ~1.4 million adsorption configurations on >2,600 crystallographically distinct surfaces. Using distribution-based similarity analysis and principal component analysis of AED moments, we identify composition-facet combinations with adsorption landscapes associated with activity and qualitative selectivity trends toward methanol, methane, formic acid, and CO. Our framework links surface structure to adsorption-based catalytic trends, providing experimentally testable composition-facet candidates for further investigation and validation.
ER -