Predicting Surface Strain Effects on Adsorption Energy with Graph Neural Networks

21 March 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Modifying the adsorption energies of reaction intermediates on different material surfaces can significantly improve heterogeneous catalysis by reducing energy barriers for intermediate elementary reaction steps. Surface strain can increase or decrease the adsorption energy depending on the surface composition, adsorbate composition, surface facet, and adsorbate site, breaking traditional scaling relationships which inhibit energy barrier alteration in reactions such as ammonia synthesis. We aim to generate a model that maps the adsorption energy response to a given input strain for a range of adsorbates and catalyst structures. After generating a training dataset of strained copper binary alloy catalyst + adsorbate complexes from the Open Catalyst Project and calculating the adsorption energy with first-principles calculations (dataset made available), we train a graph neural network to learn the relationship between catalyst + adsorbate structure, surface strain, and adsorption energy. The model successfully predicts the nature of the adsorption energy response for 85% of surface strains, outperforming simpler model baselines. Using the ammonia synthesis reaction as an example system, we identify Cu-S alloy catalysts as promising candidates for strain engineering since the majority of surface strain patterns raise the adsorption energy of the *NH intermediate. We find that the strain response of similar adsorbates on the same surface can greatly vary due to the competition between surface relaxation under strain and relaxation of the coordination environment. Our presented machine learning approach can be applied to additional datasets to identify target strain patterns that can reduce energy barriers in heterogeneous catalysis.


surface strain
graph neural networks
adsorption energy
machine learning
ammonia synthesis

Supplementary materials

Supplemental Information
Compositions of catalysts and adsorbates considered in this work; Dataset distribution for classification task; Model performance metrics on test data for ensemble linear baseline, GNN classifier, and GNN regressor; GNN model optimized hyperparameters; Correlation coefficients of dataset descriptors and adsorption energy strain response

Supplementary weblinks


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.