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arXiv 2211.08014cond-mat.mtrl-sci
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基于位隔离概念的甲烷氧化催化新材料的数据驱动设计

Data-driven design of new catalytic materials in methane oxidation based on a site isolation concept

A. Mazheika, M. Geske, M. Muller, S. A. Schunk, F. Rosowski, R. Kraehnert

摘要:

天然气(甲烷)转化为乙烷和乙烯(OCM:甲烷的氧化偶联)促进了其运输,并提供了合成更高价值化学品的方法。寻找高性能的催化剂来实现这种转化是OCM领域中大多数相应研究的主要范围。在这里,我们提出了一个通用的数据驱动的策略,用于搜索新的催化材料,特别是对OCM反应有用的材料。我们的策略是基于一致的实验测量,包括从头算热力学计算和主动筛选。基于我们的实验,这表明独特的火山型依赖于所形成的碳酸盐的稳定性的性能归因于网站隔离的概念,我们开发了一种方法,用于有效和廉价的DFT计算碳酸盐的形成能量的预测精度为0.2 eV。该方法被实施到高通量筛选方案中,该方案包括对催化剂候选物的一般要求和积极完成的人工智能部分。在筛选过程中获得的一些候选人的实验验证表明,成功地再现了最初的火山依赖性。此外,发现几种新材料的性能优于标准OCM催化剂,特别是在较低温度下。

英文摘要:

The conversion of natural gas (methane) to ethane and ethylene (OCM: oxidative coupling of methane) facilitates its transportation and provides a way to synthesize higher value chemicals. The search for high-performance catalysts to achieve this conversion is the main scope of most corresponding studies in the field of OCM. Here, we present a general data-driven strategy for the search of novel catalytic materials, focusing particularly on materials useful for the OCM reaction. Our strategy is based on consistent experimental measurements and includes ab initio thermodynamics calculations and active screening. Based on our experiments, which showed unique volcano-type dependence of the performance on the stability of formed carbonates attributed to the site isolation concept, we developed a method for efficient and inexpensive DFT calculations of the formation energies of carbonates with prediction accuracy 0.2 eV. This method was implemented into a high-throughput screening scheme, which includes both general requirements for catalyst candidates and an actively done artificial intelligence part. Experimental validation of some of the candidates obtained during the screening showed successful reproduction of the initial volcano dependence. Moreover, several new materials were found to outperform standard OCM catalysts, specifically at lower temperatures.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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