铺平了通往自动化同质催化剂设计的道路
Adarsh V Kalikadien1, Adrian Mirza1, Aydin Najl Hossaini1
1Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands.
ChemPlusChem
|January 27, 2024
概括
计算工具和机器学习加速了催化剂的设计. 自动化,数据驱动的工作流对于在催化研究中有效探索化学空间至关重要.
科学领域:
- 催化剂是一种催化剂.
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 计算工具对于现代催化剂设计和有机合成至关重要.
- 机器学习 (ML) 方法因其先进的能力而变得越来越重要.
- 高吞吐率的in silico方法提供了有价值的见解,但需要优化.
研究的目的:
- 提供对计算催化剂设计举措的概述.
- 引入用于高吞吐量的自动化工具,用于化工空间探索.
- 突出自动化和模块化在计算工作流程中的重要性.
主要方法:
- 综述各种计算催化剂设计策略.
- 开发和应用用于in silico勘探的自动化工具.
- 集成数据驱动方法与自动化,模块化工作流程.
主要成果:
- 展示用于高效化学空间探索的自动化工具.
- 确定提高计算方法的关键因素 (自动化,模块化).
- 通过集成的工作流程扩展同质催化剂设计的框架.
结论:
- 自动化,数据驱动和模块化工作流程对于推进同质催化剂设计至关重要.
- 这些方法的整合使催化剂研究能够达到前所未有的规模.
- 计算方法,特别是机器学习,显著支持实验催化.
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