Atomistic Insights into Interfacial Characteristics for Energy Conversion

该研究旨在利用通用神经网络电位分析接口。我们的目标是在La0.5Ce0.5O1.75-x还原板上负载Runanoparticle对N2的吸附和催化性能。这种非均相体系表现出传统方法无法研究的强-金属支撑相互作用（SMSI）。通过开发自动化程序，研究了200种不同催化剂配置在所有顶部位置上的N2吸附性能，总共给出了15600种不同的吸附结果。通过统计分析，确定了代表真实体系的催化剂结构。详细讨论了活化势垒与局部结构之间的关系，以确定实验观察到的高活性背后的根本因素。为了进一步研究负载纳米催化剂的界面性能，制备了不同金属和载体材料的组合。自动化过程进一步扩展到探索其他小分子的吸附特性，这些小分子是碳中性燃料合成、燃料电池中的氧还原反应和水电解中的氧析反应等关键反应的基础。

The study aims to analyze interfaces by using a universal neural network potential. We targeted the adsorption and catalytic properties of N2 on a Runanoparticle supported on a La0.5Ce0.5O1.75-x reduced slab. This heterogeneous system shows a strong-metal support interaction (SMSI), which cannot be investigated by conventional methods. By developing automated procedure, N2 adsorption properties on all ontop sites of 200 different catalyst configurations were investigated, giving 15600 different adsorption results in total. Statistical analyses was conducted to identify the catalyst structure representing the real-system. The relation between activation barrier and local structure was carefully discussed, to identify the essential factor behind the experimentally observed high activity.To further investigate the interfacial properties of supported nanocatalyst, combinations of different metal and support materials are prepared. Automated procedure was further extended to explore the adsorption properties of other small molecules, which are fundamental in key reactions such as carbon-neutral fuel synthesis, oxygen reduction reaction in fuel cells, and oxygen evolotion reaction in water electrolysis.