Xu, Chenxi;
Li, Kaiqi;
Liu, Shengchao;
Xu, Junwei;
Sharma, Subash;
Zhang, Jincan;
Mao, Boyang;
... Li, Huanxin; + view all
(2025)
Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application.
CCS Chemistry
10.31635/ccschem.025.202505577.
(In press).
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Abstract
Nonprecious-metal catalysts possess great potential to replace noble metals in catalysis; however, selecting efficient candidates through experiments is time-consuming and costly. Herein, we employed a data-driven virtual screening (VS) method to discover new electrocatalysts. Specifically, we identified Cu-N2 sites for Zn-air batteries (ZABs) in harsh electrolytes by combining VS and machine learning (ML) techniques. A thermodynamically stable and highly active Cu-N2 Lewis acid site was pinpointed using molecular dynamics (MD) simulations and density functional theory (DFT) calculations: MD simulations filtered stable structures, while DFT calculations excluded those with high overpotentials by evaluating adsorption energies of oxygen intermediates using the “Volcano plots” theory. The ML model, trained in atomic types and geometries, predicted catalysts with DFT-level accuracy. The as-predicted Cu-N2 Lewis acid site was experimentally synthesized in a hollow nitrogen-doped octahedron carbon framework (Cu-N2@HNOC), with a high Cu loading of 13.1 wt %. A ZAB with Cu-N2@HNOC as the cathode catalyst showed prolonged cycling stability and a high maximum power density of 78.1 mW/cm2. Our strategy is applicable in the quest of valuable catalysts across a wide range of applications. With the accumulation and experimental validation of datasets to improve quality, this approach is expected to accurately predict promising electrocatalysts by integrating deep ML.
| Type: | Article |
|---|---|
| Title: | Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.31635/ccschem.025.202505577 |
| Publisher version: | https://doi.org/10.31635/ccschem.025.202505577 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/3.0/ |
| Keywords: | Science & Technology, Physical Sciences, Chemistry, Multidisciplinary, Chemistry, computer virtual screening, molecular dynamics simulation, density functional theory calculation, Cu-N2-C, Zn-air batteries, EFFICIENT OXYGEN REDUCTION, FE-N-C, CARBON NANOTUBES, ELECTROCATALYSIS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Chemistry |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10211284 |
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