Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

Abstract

Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C2 yields during the OCM reaction.

Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation

“…By transforming data into networks, it then becomes possible to visually determine how atomic elements, for example, associate with factors such as CH 4 adsorption, CH 3 adsorption, and reaction energy. It has been previously demonstrated that network analysis can unveil relationships between data, which can then guide researchers by providing hints that may be useful for designing catalysts . In this sense, network analysis should be able to provide how elements or element pairs relate to CH 4 adsorption, CH 3 adsorption, and the reaction energy.…”

“…It has been previously demonstrated that network analysis can unveil relationships between data, which can then guide researchers by providing hints that may be useful for designing catalysts. 41 In this sense, network analysis should be able to provide how elements or element pairs relate to CH 4 adsorption, CH 3 adsorption, and the reaction energy.…”

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…”