Theoretical Investigation of the Structural Properties of Copper Clusters at Zinc Oxide

氧化锌中铜簇结构性质的理论研究

• 批准号: 289217282
• 负责人: Professor Dr. Jörg Behler
• 金额: --
• 依托单位: Lehrstuhl für Theoretische Chemie II
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/289217282?language=en
• 关键词: Theoretical; Investigation; Structural; Properties; Copper

Oxide-supported metal clusters play an important role in heterogeneous catalysis. A prominent example is methanol synthesis, which is catalyzed by dispersed copper and zinc oxide particles. In spite of intensive research and a wealth of experimental data, the atomistic structure of the catalyst is still unknown, and theoretical studies are urgently needed to unravel the detailed structure of the catalyst. Unfortunately, computer simulations of realistic structural models of the catalyst are severely hampered by the complexity of the system if methods like density-functional theory are used. In this project very efficient interatomic potentials based on artificial neural networks with an accuracy close to first principles calculations will be developed and employed to characterize the structural properties of the catalyst in detail. The obtained information will be combined with experimental data to provide new insights into the nature of the active sites. This is a mandatory condition for future studies of the detailed reaction mechanism of the catalytic process, which to date could be investigated using drastically simplified model systems only.

氧化物负载的金属簇合物在多相催化中起着重要的作用。一个突出的例子是甲醇合成，它是由分散的铜和锌氧化物颗粒催化的。尽管有深入的研究和丰富的实验数据，催化剂的原子结构仍然是未知的，迫切需要理论研究来解开催化剂的详细结构。不幸的是，计算机模拟的现实结构模型的催化剂是严重阻碍了系统的复杂性，如果使用的方法，如密度泛函理论。在该项目中，将开发基于人工神经网络的非常有效的原子间相互作用势，其精度接近第一原理计算，并用于详细表征催化剂的结构性质。所获得的信息将与实验数据相结合，以提供对活性位点性质的新见解。这是一个强制性的条件，为未来的研究详细的反应机理的催化过程，迄今为止，可以调查使用大大简化的模型系统。

项目成果

A flexible and adaptive grid algorithm for global optimization utilizing basin hopping Monte Carlo.

• DOI: 10.1063/1.5142363
• 发表时间: 2020-02
• 期刊: The Journal of chemical physics
• 作者: M. Paleico;J. Behler

Properties of {\alpha}-Brass Nanoparticles I: Neural Network Potential Energy Surface

• DOI: 10.1021/acs.jpcc.0c00559
• 发表时间: 2020-01
• 作者: J. Weinreich;Anton Romer;M. Paleico;Jorg Behler

Global optimization of copper clusters at the ZnO(101¯0) surface using a DFT-based neural network potential and genetic algorithms.

• DOI: 10.1063/5.0014876
• 发表时间: 2020-07
• 期刊: The Journal of chemical physics
• 作者: M. Paleico;J. Behler