Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane. Issue 14 (8th June 2021) - Nakanowatari, Sunao; Nguyen, Thanh Nhat; Chikuma, Hiroki; Fujiwara, Aya; Seenivasan, Kalaivani; Thakur, Ashutosh; Takahashi, Lauren; Takahashi, Keisuke; Taniike, Toshiaki
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 temperature and selective formation of C2 compounds at a high temperature. The obtained heuristics were used for catalyst development. The most effective was the use of a mixed support between La2 O3 and BaO, which improved the low‐temperature activity, the high‐temperature selectivity, as well as the maximum C2 yield. It was considered that La2 O3 acted as a heater and helped low‐temperature operation of BaO, which is highly selective but not active at a low temperature. Abstract : Data Science : Catalyst big data enlighten various useful heuristics when projected onto proper subsets. Combination of such heuristics could lead to novel catalyst design guidelines. Here, we successfully demonstrated the concept for the oxidative coupling of methane. Features relevant to methane activation and suppression of deep oxidation were separately extracted, and then combined to derive active and selective catalysts.