The Dataset of Predicting Strain Effects on Adsorption Energy

Strain significantly influences chemical reaction potential energy surface, which is a challenge to research due to the high-dimensional space. Here, we propose an approach to establish the relationship between the geometry, electronic density of states (DOS), and oxygen molecular adsorption energies using convolutional neural network and graph convolution neural network. By examining the adsorption energy of oxygen molecular on alloy substrates in response to surface strain, we identify that volumetric strain is the dominant parameter affecting adsorption energy and energy barriers of the reaction path. Employing features extracted from the electronic DOS, we provide physically insights by predicting adsorption energy responses to external perturbations to the electronic structure. In addition, explainable machine-learning based on moments of DOS identify the physically meaning of DOS feature and reveal the mechanochemical essence is strain regulated surface electronic environment.