Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913620643807232 |
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| author | Boonpalit, Kajjana Artrith, Nongnuch |
| author_facet | Boonpalit, Kajjana Artrith, Nongnuch |
| contents | Electrocatalytic water splitting, which produces hydrogen and oxygen through water electrolysis, is a promising method for generating renewable, carbon-free alternative fuels. However, its widespread adoption is hindered by the high costs of Pt cathodes and IrO$_{x}$/RuO$_{x}$ anode catalysts. In the search for cost-effective alternatives, barium titanate (BaTiO$_{3}$) has emerged as a compelling candidate. This inexpensive, non-toxic perovskite oxide can be synthesized from earth-abundant precursors and has shown potential for catalyzing the oxygen evolution reaction (OER) in recent studies. In this work, we explore the OER activity of pristine and Ni-doped BaTiO$_{3}$ at explicit water interfaces using metadynamics (MetaD) simulations. To enable efficient and practical MetaD for OER, we developed a machine learning interatomic potential based on artificial neural networks (ANN), achieving large-scale and long-time simulations with near-DFT accuracy. Our simulations reveal that Ni-doping enhances the catalytic activity of BaTiO$_{3}$ for OER, consistent with experimental observations, while providing mechanistic insights into this enhancement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15452 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations Boonpalit, Kajjana Artrith, Nongnuch Materials Science Disordered Systems and Neural Networks Electrocatalytic water splitting, which produces hydrogen and oxygen through water electrolysis, is a promising method for generating renewable, carbon-free alternative fuels. However, its widespread adoption is hindered by the high costs of Pt cathodes and IrO$_{x}$/RuO$_{x}$ anode catalysts. In the search for cost-effective alternatives, barium titanate (BaTiO$_{3}$) has emerged as a compelling candidate. This inexpensive, non-toxic perovskite oxide can be synthesized from earth-abundant precursors and has shown potential for catalyzing the oxygen evolution reaction (OER) in recent studies. In this work, we explore the OER activity of pristine and Ni-doped BaTiO$_{3}$ at explicit water interfaces using metadynamics (MetaD) simulations. To enable efficient and practical MetaD for OER, we developed a machine learning interatomic potential based on artificial neural networks (ANN), achieving large-scale and long-time simulations with near-DFT accuracy. Our simulations reveal that Ni-doping enhances the catalytic activity of BaTiO$_{3}$ for OER, consistent with experimental observations, while providing mechanistic insights into this enhancement. |
| title | Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations |
| topic | Materials Science Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2412.15452 |