The rational design of high-performance graphene-based single-atom electrocatalysts for the ORR using machine learning

Literature Information

Publication Date 2023-06-22
DOI 10.1039/D3CP01224G
Impact Factor 3.676
Authors

Ziqiang Chen, Hexiang Qi, Haohao Wang, Caiwei Yue, Yangqiu Liu, Zuoyin Yang, Min Pu, Ming Lei


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Abstract

In this work, high-performance two-dimensional (2D) graphene-based single-atom electrocatalysts (ZZ/ZA-MNxCy) for the oxygen reduction reaction (ORR) were screened out using machine learning (ML). A model was built for the fast prediction of electrocatalysts and two descriptors valence electron correction (VEc) and degree of construction differences (DC) were proposed to improve the accuracy of the model prediction. Two evaluation criteria, high-performance catalyst retention rate rR and high-performance catalyst occupancy rate rO, were proposed to evaluate the accuracy of ML models in high-performance catalyst screening. The addition of VEc and DC in the model could change the mean absolute error (MAEtest) of the test set, the coefficient of determination (R2test) of the test set, rO, and rR from 0.334 V, 0.683, 0.222, and 0.360 to 0.271 V, 0.774, 0.421, and 0.671, respectively. The partially screened potential high-performance ORR electrocatalysts such as ZZ-CoN4 and ZZ-CoN3C1 were also further investigated using a Density Functional Theory (DFT) method, which confirmed the accuracy of the ML model (MAE = 0.157 V, R2 = 0.821).

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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