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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.

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.

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