Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane

Abstract

In the oxidative coupling of methane (OCM), the activation of methane and the suppression of deep oxidation are in a persistent trade-off relationship, and a catalyst design strategy that balances the activity and the selectivity is desired. In this study, we analyzed a random catalyst dataset for OCM that was earlier obtained by high-throughput experimentation, and extracted heuristics such as elements, supports, and their combinations related to methane activation at a low temper-ature and selective formation of C 2 compounds at a high temperature. The obtained heuristics were used for catalyst development. The most effective was the use of a mixed support between La 2 O 3 and BaO, which improved the lowtemperature activity, the high-temperature selectivity, as well as the maximum C 2 yield. It was considered that La 2 O 3 acted as a heater and helped low-temperature operation of BaO, which is highly selective but not active at a low temperature.

Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter

“…It is particularly interesting that based on none/La 2 O 3 performance as a comparison standard the Ba and La impregnations induce positive features at C 2 yield, whereas Fa, Mn, and La are negative effects at C 2 yield. Preferred combination effects of Ba and La 2 O 3 have been described in earlier reports. , However, the best performance of 14.2% C 2 yield with 65.6% selectivity at 800 °C achieved Ba/La 2 O 3 does not satisfy the superior performance obtained over Mn-promoted three-element-supported La 2 O 3 , particularly LiFeBa-5Mn/La 2 O 3 . Accordingly, the synergetic effect mediates the novel OCM performance among Li, Fe, Ba, Mn, and La 2 O 3 .…”

“…Preferred combination effects of Ba and La 2 O 3 have been described in earlier reports. 41,42 However, the best performance of 14.2% C 2 yield with 65.6% selectivity at 800 °C achieved Ba/La 2 O 3 does not satisfy the superior performance obtained over Mn-promoted threeelement-supported La 2 O 3 , particularly LiFeBa-5Mn/La 2 O 3 . Accordingly, the synergetic effect mediates the novel OCM performance among Li, Fe, Ba, Mn, and La 2 O 3 .…”

Machine learning for design principles for single atom catalysts towards electrochemical reactions

“…77 In addition, several studies have recently reported data mining from the literature for the ML-assisted design and discovery of new heterogeneous catalysts for oxidative coupling of methane. [78][79][80][81][82][83] Fig. 3 shows the workow for the summary of a data mining sequence from the literature.…”

High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La2O3-based catalysts for the oxidative coupling of methane

“…Indeed, within the last a few years, the combined use of tailor-made experimental datasets obtained with a high throughput screening (HTS) machine and data analytic techniques such as multi-output ML and network profiling have presented new avenues for the design and elucidation of the nature and catalytic activity of catalysts, in particular OCM. [22][23][24][25][26][27][28][29][30][31][32] Several parallel reactor systems for effective data collection in a fixed bed catalytic reaction have become commercially available 33,34 and others have been proposed in the literature. 22,[35][36][37] Nevertheless, these systems still require special skills and entail high costs for operation and construction similar to the conventional modes of catalyst investigation.…”

“…Indeed, within the last a few years, the combined use of tailor-made experimental datasets obtained with a high throughput screening (HTS) machine and data analytic techniques such as multi-output ML and network profiling have presented new avenues for the design and elucidation of the nature and catalytic activity of catalysts, in particular OCM. 22–32…”