A transferable machine-learning scheme from pure metals to alloys for predicting adsorption energies
| العنوان: | A transferable machine-learning scheme from pure metals to alloys for predicting adsorption energies |
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| المؤلفون: | Zhiwen Chen, Qing Jiang, Ze Yang, Wang Gao, Xin Li, Bo Li |
| المصدر: | Journal of Materials Chemistry A. 10:872-880 |
| بيانات النشر: | Royal Society of Chemistry (RSC), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Materials science, Valence (chemistry), Renewable Energy, Sustainability and the Environment, business.industry, Pure metals, Alloy, Intermetallic, General Chemistry, engineering.material, Machine learning, computer.software_genre, Catalysis, Electronegativity, Adsorption, Transition metal, engineering, General Materials Science, Artificial intelligence, business, computer |
| الوصف: | Alloys present the great potential in catalysis because of their adjustable compositions, structures and element distributions, which unfortunately also limit the fast screening of the potential alloy catalysts. Machine learning methods are able to tackle the multi-variable issues but still cannot yet predict the complex alloy catalysts from the properties of pure metals due to the lack of universal descriptors. Herein we propose a transferable machine-learning model based on the intrinsic properties of substrates and adsorbates, which can predict the adsorption energies of single-atom alloys (SAAs), AB intermetallics (ABs) and high-entropy alloys (HEAs), simply by training the properties of transition metals (TMs). Furthermore, this model builds the structure-activity relationship of the adsorption energies on alloys from the perspective of machine learning, which reveals the role of the surface atoms’ valence, electronegativity and coordination and the adsorbates’ valence in determining the adsorption energies. This transferable scheme advances the understanding of the adsorption mechanism on alloys and the rapid design of alloy catalysts. |
| تدمد: | 2050-7496 2050-7488 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::1c714a71083031002cdaf086098bfc9bTest https://doi.org/10.1039/d1ta09184kTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi...........1c714a71083031002cdaf086098bfc9b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20507496 20507488 |
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