Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts
Summary: The oxygen evolution reaction (OER) is a critical reaction for energy-related applications, yet suffers from its slow kinetics and large overpotential. It is desirable to develop effective OER electrocatalysts, such as single-atom catalysts (SACs). Here, we demonstrate machine learning (ML)...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-05-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004221003667 |
| _version_ | 1818646980037967872 |
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| author | Lianping Wu Tian Guo Teng Li |
| author_facet | Lianping Wu Tian Guo Teng Li |
| author_sort | Lianping Wu |
| collection | DOAJ |
| description | Summary: The oxygen evolution reaction (OER) is a critical reaction for energy-related applications, yet suffers from its slow kinetics and large overpotential. It is desirable to develop effective OER electrocatalysts, such as single-atom catalysts (SACs). Here, we demonstrate machine learning (ML)-accelerated prediction of OER overpotential of all transition metals. Based on density functional theory (DFT) calculations of 15 species of SACs, we design a topological information-based ML model to map the OER overpotentials with atomic properties of the corresponding SACs. The trained ML model not only yields remarkable prediction precision (relative error of 6.49%) but also enables a 130,000-fold reduction of prediction time in comparison with pure DFT calculation. Furthermore, an intrinsic descriptor that correlates the overpotential of an SAC with its atomic properties is revealed. The approach and results from this study can be readily applicable to screen other SACs and significantly accelerate the design of high-performance catalysts for many other reactions. |
| first_indexed | 2024-12-17T00:55:15Z |
| format | Article |
| id | doaj.art-d300724aba1642df8af1b2583b014df2 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-12-17T00:55:15Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-d300724aba1642df8af1b2583b014df22022-12-21T22:09:38ZengElsevieriScience2589-00422021-05-01245102398Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalystsLianping Wu0Tian Guo1Teng Li2Department of Mechanical Engineering, University of Maryland, College Park, MD, USA 20742Department of Mechanical Engineering, University of Maryland, College Park, MD, USA 20742Department of Mechanical Engineering, University of Maryland, College Park, MD, USA 20742; Corresponding authorSummary: The oxygen evolution reaction (OER) is a critical reaction for energy-related applications, yet suffers from its slow kinetics and large overpotential. It is desirable to develop effective OER electrocatalysts, such as single-atom catalysts (SACs). Here, we demonstrate machine learning (ML)-accelerated prediction of OER overpotential of all transition metals. Based on density functional theory (DFT) calculations of 15 species of SACs, we design a topological information-based ML model to map the OER overpotentials with atomic properties of the corresponding SACs. The trained ML model not only yields remarkable prediction precision (relative error of 6.49%) but also enables a 130,000-fold reduction of prediction time in comparison with pure DFT calculation. Furthermore, an intrinsic descriptor that correlates the overpotential of an SAC with its atomic properties is revealed. The approach and results from this study can be readily applicable to screen other SACs and significantly accelerate the design of high-performance catalysts for many other reactions.http://www.sciencedirect.com/science/article/pii/S2589004221003667Artificial IntelligenceCatalysisElectrochemistryEnergy Materials |
| spellingShingle | Lianping Wu Tian Guo Teng Li Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts iScience Artificial Intelligence Catalysis Electrochemistry Energy Materials |
| title | Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts |
| title_full | Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts |
| title_fullStr | Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts |
| title_full_unstemmed | Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts |
| title_short | Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts |
| title_sort | machine learning accelerated prediction of overpotential of oxygen evolution reaction of single atom catalysts |
| topic | Artificial Intelligence Catalysis Electrochemistry Energy Materials |
| url | http://www.sciencedirect.com/science/article/pii/S2589004221003667 |
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