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    Extracting Knowledge from Data through Catalysis Informatics
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    • Andrew J. Medford*
      Andrew J. Medford
      School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30318 United States
      *E-mail: [email protected]
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      Orcidhttp://orcid.org/0000-0001-8311-9581
    • M. Ross Kunz
      M. Ross Kunz
      Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
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    • Sarah M. Ewing
      Sarah M. Ewing
      Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
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    • Tammie Borders
      Tammie Borders
      Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
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    • Rebecca Fushimi*
      Rebecca Fushimi
      Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
      Center for Advanced Energy Studies, 995 University Boulevard, Idaho Falls, Idaho 83401, United States
      *E-mail: [email protected]
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    ACS Catalysis

    Cite this: ACS Catal. 2018, 8, 8, 7403–7429
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    https://doi.org/10.1021/acscatal.8b01708
    Published June 22, 2018
    Copyright © 2018 American Chemical Society
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    Abstract

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    Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and materials informatics but with distinctive challenges arising from the dynamic, surface-sensitive, and multiscale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work, we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data–information–knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and microkinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, including improved quantitative coupling of experiment/theory, advanced microkinetic models, and the development of open-source software tools. Ultimately, integration of existing chemical and physical models with emerging statistical and computational tools presents a promising route toward the automated design, discovery, and optimization of heterogeneous catalytic processes.

    Copyright © 2018 American Chemical Society

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    76. Srinivas Rangarajan. Artificial intelligence in catalysis. 2024, 167-204. https://doi.org/10.1016/B978-0-323-99135-3.00002-6
    77. Ali Behrad Vakylabad. Kinetic study and reaction mechanisms in homogeneous catalysis. 2024, 1-36. https://doi.org/10.1016/B978-0-443-15181-1.00010-9
    78. Mehmet Ali Balcı, Ömer Akgüller, Larissa M. Batrancea, Anca Nichita. The Impact of Turkish Economic News on the Fractality of Borsa Istanbul: A Multidisciplinary Approach. Fractal and Fractional 2024, 8 (1) , 32. https://doi.org/10.3390/fractalfract8010032
    79. Ganesan Elumalai, Satoshi Tominaka. Universal metrics for predicting the activity of a wide range of fuel-cell catalysts. Science and Technology of Advanced Materials: Methods 2023, 3 (1) https://doi.org/10.1080/27660400.2023.2278319
    80. Ziqiu Chen, Emmanuel Ejiogu, Baron Peters. Quantifying synergy for mixed end-scission and random-scission catalysts in polymer upcycling. Reaction Chemistry & Engineering 2023, 9 (1) , 139-147. https://doi.org/10.1039/D3RE00390F
    81. Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon. Scope of machine learning in materials research—A review. Applied Surface Science Advances 2023, 18 , 100523. https://doi.org/10.1016/j.apsadv.2023.100523
    82. Jiahua Zhou, Piaoping Yang, Pavel A. Kots, Maximilian Cohen, Ying Chen, Caitlin M. Quinn, Matheus Dorneles de Mello, J. Anibal Boscoboinik, Wendy J. Shaw, Stavros Caratzoulas, Weiqing Zheng, Dionisios G. Vlachos. Tuning the reactivity of carbon surfaces with oxygen-containing functional groups. Nature Communications 2023, 14 (1) https://doi.org/10.1038/s41467-023-37962-3
    83. Linkai Han, Zhonghua Xiang. Intelligent design and synthesis of energy catalytic materials. Fundamental Research 2023, 51 https://doi.org/10.1016/j.fmre.2023.10.012
    84. Kexin Bi, Tujie Chen, Tong Qiu, Xu Ji, Yiyang Dai. Reaction network simplification and key routes extraction for steam cracking process. Fuel 2023, 352 , 129030. https://doi.org/10.1016/j.fuel.2023.129030
    85. Quan Zou, Akiyoshi Kuzume, Masataka Yoshida, Takane Imaoka, Kimihisa Yamamoto. Machine Learning Accelerated Discovery of Subnanoparticles for Electrocatalytic Hydrogen Evolution. Chemistry Letters 2023, 52 (10) , 828-831. https://doi.org/10.1246/cl.230310
    86. Bo Liang, Xiang-Yu Xiao, Zong-Yin Song, Yong-Yu Li, Xin Cai, Rui-Ze Xia, Shi-Hua Chen, Meng Yang, Pei-Hua Li, Chu-Hong Lin, Xing-Jiu Huang. Revealing the solid-solution interface interference behaviors between Cu2+ and As(III) via partial peak area analysis of simulations and experiments. Analytica Chimica Acta 2023, 1277 , 341676. https://doi.org/10.1016/j.aca.2023.341676
    87. Hossein Mashhadimoslem, Mobin Safarzadeh Khosrowshahi, Mostafa Delpisheh, Caillean Convery, Mashallah Rezakazemi, Tejraj M. Aminabhavi, Milad Kamkar, Ali Elkamel. Green ammonia to Hydrogen: Reduction and oxidation catalytic processes. Chemical Engineering Journal 2023, 474 , 145661. https://doi.org/10.1016/j.cej.2023.145661
    88. Gazali Tanimu, Jimoh Olawale Ajadi, Yussif Yahaya, Hassan Alasiri, Nurudeen A. Adegoke. Developing Machine Learning Models for Catalysts in Oxidative Dehydrogenation of n‐butane. ChemCatChem 2023, 15 (17) https://doi.org/10.1002/cctc.202300598
    89. Keerthana Vellayappan, Yifei Yue, Kang Hui Lim, Keyu Cao, Ji Yang Tan, Shuwen Cheng, Tianchang Wang, Terry Z.H. Gani, Iftekhar A. Karimi, Sibudjing Kawi. Impacts of catalyst and process parameters on Ni-catalyzed methane dry reforming via interpretable machine learning. Applied Catalysis B: Environmental 2023, 330 , 122593. https://doi.org/10.1016/j.apcatb.2023.122593
    90. Clara Patricia Marshall, Julia Schumann, Annette Trunschke. Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use. Angewandte Chemie 2023, 135 (30) https://doi.org/10.1002/ange.202302971
    91. Clara Patricia Marshall, Julia Schumann, Annette Trunschke. Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use. Angewandte Chemie International Edition 2023, 62 (30) https://doi.org/10.1002/anie.202302971
    92. Antoine Bonnefont. Deciphering the effect of pH on electrocatalytic reactions with kinetic modeling. Current Opinion in Electrochemistry 2023, 39 , 101294. https://doi.org/10.1016/j.coelec.2023.101294
    93. Jaehyun Kim, Ho Won Jang. Can Artificial Intelligence Boost Developing Electrocatalysts for Efficient Water Splitting to Produce Green Hydrogen?. Korean Journal of Materials Research 2023, 33 (5) , 175-188. https://doi.org/10.3740/MRSK.2023.33.5.175
    94. Gabriel S. Gusmão, Adhika P. Retnanto, Shashwati C. da Cunha, Andrew J. Medford. Kinetics-informed neural networks. Catalysis Today 2023, 417 , 113701. https://doi.org/10.1016/j.cattod.2022.04.002
    95. Evgeniy A. Redekop, Gregory S. Yablonsky, John T. Gleaves. Truth is, we all are transients: A perspective on the time-dependent nature of reactions and those who study them. Catalysis Today 2023, 417 , 113761. https://doi.org/10.1016/j.cattod.2022.05.026
    96. Keisuke Takahashi, Junya Ohyama, Shun Nishimura, Jun Fujima, Lauren Takahashi, Takeaki Uno, Toshiaki Taniike. Catalysts informatics: paradigm shift towards data-driven catalyst design. Chemical Communications 2023, 59 (16) , 2222-2238. https://doi.org/10.1039/D2CC05938J
    97. Haobo Li, Yan Jiao, Kenneth Davey, Shi‐Zhang Qiao. Data‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts. Angewandte Chemie 2023, 135 (9) https://doi.org/10.1002/ange.202216383
    98. Haobo Li, Yan Jiao, Kenneth Davey, Shi‐Zhang Qiao. Data‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts. Angewandte Chemie International Edition 2023, 62 (9) https://doi.org/10.1002/anie.202216383
    99. Tianyou Mou, Hemanth Somarajan Pillai, Siwen Wang, Mingyu Wan, Xue Han, Neil M. Schweitzer, Fanglin Che, Hongliang Xin. Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nature Catalysis 2023, 6 (2) , 122-136. https://doi.org/10.1038/s41929-023-00911-w
    100. Tulsi Satyavir Dabodiya, Jayant Kumar, Arumugam Vadivel Murugan. Contemplation of Photocatalysis Through Machine Learning. 2023, 221-232. https://doi.org/10.1007/978-981-99-0393-1_10
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    Cite this: ACS Catal. 2018, 8, 8, 7403–7429
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