Fast and accurate prediction of adsorption energy of AgPd nanoalloys by deep learning potentials and neural networks.

ABSTRACT: The accurate prediction of adsorption energy plays a crucial role in the design of efficient catalysts. However, the high-throughput prediction of adsorption energies based on first-principles methods presents significant challenges, hindering the rapid acceleration of catalyst development. In this study, a dataset of F atom adsorption energies was constructed, consisting of 1087 adsorption configurations on AgPd clusters, using the Ag-Pd-F deep learning potential. We employed four distinct deep neural network architectures, namely, Crystal Graph Convolutional Neural Networks (CGCNN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Multilayer Perceptrons (MLP), to establish an accurate predictive model for F atom adsorption on 38-atom AgPd nanoclusters. Amo

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12780089 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature