标题： 通过机器学习加速模拟的镍掺杂钛酸钡上的氧析出反应机制见解 显示英文标题

标题： Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations

摘要： 电催化水分解通过水电解产生氢气和氧气，是一种生成可再生、无碳替代燃料的有前景的方法。 然而，其广泛应用受到Pt阴极和IrO$_{x}$/RuO$_{x}$阳极催化剂高成本的阻碍。 在寻找经济有效的替代品的过程中，钛酸钡（BaTiO$_{3}$）已成为一个有吸引力的候选材料。 这种廉价、无毒的钙钛矿氧化物可以从丰富地球的前驱体中合成，并且在最近的研究中显示出催化析氧反应（OER）的潜力。 在这项工作中，我们使用元动力学（MetaD）模拟研究了原始和Ni掺杂的BaTiO$_{3}$在明确水界面处的OER活性。 为了实现高效且实用的OER MetaD，我们开发了一种基于人工神经网络（ANN）的机器学习原子间势，实现了具有接近DFT精度的大规模和长时间模拟。 我们的模拟结果表明， Ni掺杂增强了BaTiO$_{3}$对OER的催化活性，这与实验观察一致，并提供了对此增强的机制性见解。 显示英文摘要

摘要： 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.