通过注意力增强模型解码高催化剂中的活性位点
Liang Yin1,2,3, Tiantian Ma4, Zibo Zhu1,5
1State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai 200050, China.
Science advances
|February 13, 2026
概括
研究人员开发了一种预测模型,用于在催化剂中找到活性位点. 这导致了TiFeNiZn-CoOOH的发现,TiFeNiZn-CoOOH是氧演化反应 (OER) 的高性能催化剂.
科学领域:
- 材料科学与工程 材料科学与工程
- 催化剂是一种催化剂.
- 计算化学的计算化学
背景情况:
- 确定活性位点对于催化剂优化至关重要,特别是在复杂的高材料中.
- 预测这些材料中的催化活性和过度潜能,由于众多随机地点,存在重大挑战.
研究的目的:
- 开发一种先进的预测模型,以准确识别活跃站点及其相关的过度潜力.
- 选大量的高催化剂以获得最佳的氧化演化反应 (OER) 性能.
- 确定增强催化活性的特定元素组成和协调环境.
主要方法:
- 开发了一个增强注意力的多目标预测模型.
- 应用该模型来预测高性CoOOH材料中的OER过度潜力和兴奋剂形成能量.
- 选了17,500种潜在的催化剂,随后进行了自动合成和实验验证.
主要成果:
- 从选中确定了8种具有最佳催化活性的催化剂.
- 发现了TiFeNiZn-CoOOH,在100mA/cm2时表现出263mV的异常OER超电位.
- 证实了 Zn 的高活跃位点占用概率和 [CoNiZn] 协调在最大限度地减少过度潜力的作用.
结论:
- 开发的预测模型精确地识别了高催化剂中的活性位点和过度潜力.
- 合并和特定的协调环境通过激活差距状态,显著增强了OER催化活性.
- 这种方法为发现具有可预测结构的高性能催化剂提供了一个强大的框架.
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