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可转移的,活的数据集用于预测全球最低能量纳米集群几何

Bart Klumpers¹, Emiel J M Hensen¹, Ivo A W Filot¹

¹Laboratory of Inorganic Materials and Catalysis, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands.

Journal of chemical theory and computation

|July 24, 2024

概括

数据驱动的方法加速了纳米集群几何学的研究,用于催化. 可转移的数据集可以将计算成本降低90%以上,特别是在复杂的合金系统中.

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
计算化学计算化学

背景情况:

纳米集群几何学对于理解催化活性和活性位点至关重要.
不同质的催化包括金属支相互作用,改变集群特性.
模拟各种纳米集群形状的计算成本往往太高了.

研究的目的:

为高效的纳米集群几何建模开发数据驱动的方法.
为了降低在催化中探索结构配置的计算成本.
在不同的金属系统中利用可转移的结构信息.

主要方法:

使用来自不同系统的重叠集群形状构建"活"数据集.
用于初始化新集群研究的数据集的代改进.
应用数据驱动方法来降低模型构建成本.

主要成果:

利用可转移的结构信息将模型建造成本降低了90%以上.
该方法对研究具有较大的配置空间的合金系统有显著的好处.
为初始化纳米集群研究建立了一个低成本的途径.

结论:

数据驱动的方法和可转移的结构数据为纳米集群建模提供了高效的方法.
这一策略大大降低了催化研究中的计算开销.
这些发现对复杂合金催化剂的开发特别有影响.

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