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For: Sugiyama K, Nguyen TN, Nakanowatari S, Miyazato I, Taniike T, Takahashi K. Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning. ChemCatChem 2020. [DOI: 10.1002/cctc.202001680] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Number Cited by Other Article(s)
1
Liu L, Corma A. Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles. Chem Rev 2023;123:4855-4933. [PMID: 36971499 PMCID: PMC10141355 DOI: 10.1021/acs.chemrev.2c00733] [Citation(s) in RCA: 63] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Indexed: 03/29/2023]
2
Ishioka S, Fujiwara A, Nakanowatari S, Takahashi L, Taniike T, Takahashi K. Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane. ACS Catal 2022. [DOI: 10.1021/acscatal.2c03142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
3
Predicting the photosynthetic ammonia on nanoporous cobalt zirconate via graph convolutional neural networks. MOLECULAR CATALYSIS 2022. [DOI: 10.1016/j.mcat.2022.112565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
4
Rodriguez JA, Rui N, Zhang F, Senanayake SD. In Situ Studies of Methane Activation Using Synchrotron-Based Techniques: Guiding the Conversion of C–H Bonds. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00941] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
5
Chen K, Tian H, Li B, Rangarajan S. A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data. AIChE J 2022. [DOI: 10.1002/aic.17584] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
6
Nishimura S, Le SD, Miyazato I, Fujima J, Taniike T, Ohyama J, Takahashi K. High-Throughput Screening and Literature Data Driven Machine Learning Assisting Investigation of Multi-component La2O3-based Catalysts for Oxidative Coupling of Methane. Catal Sci Technol 2022. [DOI: 10.1039/d1cy02206g] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
7
Nguyen TN, Seenivasan K, Nakanowatari S, Mohan P, Tran TPN, Nishimura S, Takahashi K, Taniike T. Factors to influence low-temperature performance of supported Mn–Na2WO4 in oxidative coupling of methane. MOLECULAR CATALYSIS 2021. [DOI: 10.1016/j.mcat.2021.111976] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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