文献类型：期刊文献

英文题名：Minimising the Demand for High-Fidelity Training Data towards Chemically Accurate Adsorption Energy Predictions

作者：Zhang, Zhihao[1]; Cao, Xiao-Ming[1,2]

机构：[1] State Key Laboratory of Green Chemical Engineering and Industrial Catalysis, Centre for Computational Chemistry, Research Institute of Industrial Catalysis, East China University of Science and Technology, Shanghai, 200237, China; [2] State Key Laboratory of Synergistic Chem-Bio Synthesis, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250332873)

语种：英文

外文关键词：Adsorption - Catalysts - Computation theory - Deep learning - Learning systems - Personnel training

摘要：Adsorption energy is a critical descriptor for high-throughput screening of heterogeneous catalysts and electrode materials. However, precise experimental data are scarce due to the complexity of experiments, while high-fidelity density functional theory (DFT) calculations remain computationally expensive for large-scale material screening. Machine learning models trained on DFT data have emerged as a promising alternative but face challenges such as functional dependency and limited high-fidelity labelled data. Herein, we present DOS Transformer for Adsorption (DOTA), a functional-independent deep learning model established on the map between local density of states (LDOS) and adsorption energy. DOTA integrates multi-head self-attention mechanisms with LDOS feature engineering to capture latent orbital interaction patterns, enabling it to unify multi-fidelity and multi-source data. This minimises the demand for high-fidelity training data. Consequently, the predictive adsorption energy could reach chemical accuracy, requiring less than five high-fidelity experimental adsorption energies for model training. DOTA also resolves long-standing challenges, such as the"CO puzzle", and outperforms traditional theories, including the d-band centre and Fermi softness models. It provides a robust framework for efficient catalyst and electrode screening, bridging the gap between computational and experimental data. Copyright ? 2025, The Authors. All rights reserved.