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Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts
Chemical Science ( IF 7.6 ) Pub Date : 2021-09-22 , DOI: 10.1039/d1sc04390k Lauren Takahashi 1 , Thanh Nhat Nguyen 2 , Sunao Nakanowatari 2 , Aya Fujiwara 2 , Toshiaki Taniike 2 , Keisuke Takahashi 1
Chemical Science ( IF 7.6 ) Pub Date : 2021-09-22 , DOI: 10.1039/d1sc04390k Lauren Takahashi 1 , Thanh Nhat Nguyen 2 , Sunao Nakanowatari 2 , Aya Fujiwara 2 , Toshiaki Taniike 2 , Keisuke Takahashi 1
Affiliation
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Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C2 yields more systematically, high throughput experiments are conducted in an effort to mass-produce catalyst-related data in a way that provides more consistency and structure. Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C2 yields during the OCM reaction. Transforming high-throughput data in this manner has resulted in a representation of catalyst data that is more intuitive to use and also has resulted in the successful design of a myriad of catalysts that elicit high C2 yields, several of which resulted in yields greater than those originally reported in the high-throughput data. Thus, transforming high-throughput catalytic data into catalyst design-friendly maps provides a new method of catalyst design that is more efficient and has a higher likelihood of resulting in high performance catalysts.
中文翻译：
基于催化剂大数据构建甲烷氧化偶联催化剂知识网络设计催化剂
设计用于甲烷氧化偶联 (OCM) 反应的高性能催化剂通常受到催化剂数据不一致的阻碍，这通常导致难以提取信息，例如元素对催化剂性能的组合影响以及难以达到超过特定阈值的产率. 为了更系统地研究C 2产率，进行了高通量实验以努力以提供更多一致性和结构的方式大量产生催化剂相关数据。应用图论是为了将高通量数据转换为网络的潜在趋势可视化，然后将其用于设计可能导致高 C 2 的新催化剂OCM 反应过程中的产率。以这种方式转换高通量数据导致了使用更直观的催化剂数据的表示，并且还导致成功设计了引发高 C 2产率的无数催化剂，其中一些导致产率大于那些最初在高通量数据中报告的。因此，将高通量催化数据转化为催化剂设计友好的地图提供了一种新的催化剂设计方法，该方法更有效并且更有可能产生高性能催化剂。
更新日期：2021-09-22
中文翻译：
基于催化剂大数据构建甲烷氧化偶联催化剂知识网络设计催化剂
设计用于甲烷氧化偶联 (OCM) 反应的高性能催化剂通常受到催化剂数据不一致的阻碍，这通常导致难以提取信息，例如元素对催化剂性能的组合影响以及难以达到超过特定阈值的产率. 为了更系统地研究C 2产率，进行了高通量实验以努力以提供更多一致性和结构的方式大量产生催化剂相关数据。应用图论是为了将高通量数据转换为网络的潜在趋势可视化，然后将其用于设计可能导致高 C 2 的新催化剂OCM 反应过程中的产率。以这种方式转换高通量数据导致了使用更直观的催化剂数据的表示，并且还导致成功设计了引发高 C 2产率的无数催化剂，其中一些导致产率大于那些最初在高通量数据中报告的。因此，将高通量催化数据转化为催化剂设计友好的地图提供了一种新的催化剂设计方法，该方法更有效并且更有可能产生高性能催化剂。
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