电化学过氧化合成的通用催化剂设计框架,由局部原子环境描述器提供便利
Zhijian Liu1, Yan Liu1, Yuqi Zhang2
1School of Energy and Power Engineering, North China Electric Power University, Baoding, 071003, P.R. China.
Angewandte Chemie (International ed. in English)
|December 8, 2025
概括
一个新的框架使用机器学习和以原子为中心的描述器来设计用于氧化水的高性能催化剂. 这种方法加快了有效催化剂的发现,如LiScO2用于过氧化的生产.
科学领域:
- 催化剂是一种催化剂.
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 设计高性能催化剂是复杂的,因为不同的材料结构和活性站点.
- 要克服这些挑战,需要一个通用的催化剂设计框架.
研究的目的:
- 开发一种使用机器学习和基于描述器的建模的高性能催化剂的通用设计框架.
- 准确预测两电子水氧化反应 (2e-WOR) 的催化剂性能.
主要方法:
- 将加权原子中心对称函数 (wACSF) 描述符与机器学习 (ML) 集成.
- 使用微动力学建模和高通量选.
- 开发ML模型来预测各种催化剂类型的吸附自由能量 (ΔGOH*和ΔGO*).
主要成果:
- ML模型准确地预测了各种催化剂家族的吸附自由能量 (R2 = 0.84对于 ΔGOH*,R2 = 0.91对于 ΔGO*).
- 建立了2e-WOR的普世火山模型,与实验数据有很好的一致性.
- 高通量选确定了LiScO2,实现了90%的H2O2法拉第效率和168小时的稳定性.
结论:
- 开发的通用框架使电化学反应的合理催化剂设计成为可能.
- 这种方法有助于在不同材料类别中有效地发现新型催化剂.
- 该框架在数字催化剂平台 (DigCat) 中实施,以获得更广泛的应用.
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