Toward Instant Prediction of Metal Adatom Diffusion Barriers for Understanding of Sintering and Catalyst Durability

Here, we present an efficient method for calculating adatom potential energy surfaces (PESs) and the activation barriers involved in the elementary steps of surface diffusion. Our method estimates a coupled potential energy profile based on the PESs of the decoupled processes and assumes bond order conservation. The associated barriers of migration were compared and validated against calculated barriers on common FCC(100) metal surfaces of Ag, Au, Cu, Pd, Pt, Rh, Ni, and Ir metals. The barrier prediction accuracy quantified by the mean absolute error (MAE) using the method is 0.12 eV, which is comparable to the accuracy of TS barriers by using Brønsted–Evans–Polanyi (BEP) relations. Given their high accuracy, the approximated activation barriers have the potential to predict the sintering and restructuring of metallic systems on-the-fly. Therefore, we believe that the model will help to improve and speed up the understanding of surface restructuring and sintering in heterogeneous catalysis.

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Publication Details

Journal
The Journal of Physical Chemistry C
Published
2025-12-31
DOI
https://doi.org/10.1021/acs.jpcc.5c07376
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Toward Instant Prediction of Metal Adatom Diffusion Barriers for Understanding of Sintering and Catalyst Durability

Frank Abild‐Pedersen, Shyama Charan Mandal, Shyam Deo, Shikha Saini
The Journal of Physical Chemistry C
Machine Learning in Materials Science
article

Toward Instant Prediction of Metal Adatom Diffusion Barriers for Understanding of Sintering and Catalyst Durability

Frank Abild‐Pedersen, Shyama Charan Mandal, Shyam Deo, Shikha Saini
article en

Abstract

Here, we present an efficient method for calculating adatom potential energy surfaces (PESs) and the activation barriers involved in the elementary steps of surface diffusion. Our method estimates a coupled potential energy profile based on the PESs of the decoupled processes and assumes bond order conservation. The associated barriers of migration were compared and validated against calculated barriers on common FCC(100) metal surfaces of Ag, Au, Cu, Pd, Pt, Rh, Ni, and Ir metals. The barrier prediction accuracy quantified by the mean absolute error (MAE) using the method is 0.12 eV, which is comparable to the accuracy of TS barriers by using Brønsted–Evans–Polanyi (BEP) relations. Given their high accuracy, the approximated activation barriers have the potential to predict the sintering and restructuring of metallic systems on-the-fly. Therefore, we believe that the model will help to improve and speed up the understanding of surface restructuring and sintering in heterogeneous catalysis.

The Journal of Physical Chemistry CVol. 130(2)
SLAC National Accelerator Laboratory (US), Interface (United States) (US), Stanford University (US)
Affordable and clean energy
Openalex Percentile: Top 47%
Machine Learning in Materials Science
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Toward Instant Prediction of Metal Adatom Diffusion Barriers for Understanding of Sintering and Catalyst Durability — Frank Abild‐Pedersen, Shyama Charan Mandal, et al. · The Journal of Physical Chemistry C (2025) | TGRS Research Map | TGRS