用实验和计算数据开发用于异质催化物的机器学习
Carlota Bozal-Ginesta1,2,3, Sergio Pablo-García4,5,6,7, Changhyeok Choi4,5
1Nanoionics and Fuel Cells group, Catalonia Institute for Energy Research, Barcelona, Spain. carlota.bozalginesta@empa.ch.
Nature reviews. Chemistry
|July 18, 2025
概括
机器学习 (ML) 模型可以使用计算数据预测催化剂性能. 本综述分析了将ML与高通量方法集成为固体异质催化物的趋势,使用实验数据和计算数据.
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:
- 机器学习 (ML) 在大型数据集中的模式识别方面表现出色,包括催化剂性能预测.
- 目前在催化中的ML模型主要使用高通量量子化学,实验验证有限.
- 简化的计算模型和稀缺的实验数据阻碍了ML在催化中的成功.
研究的目的:
- 审查整合高通量方法和ML用于固体异质催化物的研究.
- 分析ML模型描述符,材料,反应和数据集大小的趋势.
- 通过使用不同趋势的R平方值来评估ML模型的性能.
主要方法:
- 对异质催化中的ML现有文献的系统分析.
- 基于输入/输出描述符,材料,反应和数据集大小的研究分类.
- 基于确定趋势的ML模型性能 (R平方值) 的比较.
主要成果:
- 在异质催化 ML 应用中确定了关键趋势.
- 强调了整合实验和计算数据的重要性.
- 根据各种因素对模型性能进行了比较分析.
结论:
- 集成高吞吐量计算和机器学习对推进异质催化有很大的前景.
- 解决计算模型和实验数据的局限性对于更广泛的ML采用至关重要.
- 本综述提供了关于 ML 在催化剂设计中的当前趋势和未来方向的见解.
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