活动站点
熵(时间箭头)
催化作用
材料科学
寄主(生物学)
密度泛函理论
吸附
化学物理
生物系统
计算化学
化学
物理化学
热力学
物理
生态学
生物化学
生物
作者
Meena Rittiruam,Pisit Khamloet,Potipak Tantitumrongwut,Tinnakorn Saelee,Patcharaporn Khajondetchairit,Jakapob Noppakhun,Annop Ektarawong,Björn Alling,Supareak Praserthdam,Piyasan Praserthdam
标识
DOI:10.1002/adts.202300327
摘要
Abstract Active‐site models comprise miniature active sites on the host element, providing one of effective descriptors for screening high‐entropy‐alloy (HEA) catalysts using machine learning. This study investigates the impact of host elements on the electronic properties of active sites via density functional theory (DFT), where the active‐site model is used in the HEA electrocatalysts. Also, the appropriate host element selection significantly affects the system's surface structures, electronic, and catalytic properties that adsorbate adsorption energy, d‐band center, Bader charge, Zero‐point energy, and entropy are used as accuracy verification parameters compared to the original surface. Ultimately, the novel guideline for active‐site model construction is proposed using the simple example of PtPdFeCoNi high‐entropy alloys. This investigation demonstrates that the host element selection is a crucial parameter to the active‐site models, influencing the electronic structure and electrocatalytic properties.
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