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de Arriba A, Sánchez G, Sánchez-Tovar R, Concepción P, Fernández-Domene R, Solsona B, López Nieto JM. On the selectivity to ethylene during ethane ODH over M1-based catalysts. Catal Today 2023. [DOI: 10.1016/j.cattod.2023.114122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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de Arriba A, Solsona B, Dejoz AM, Concepción P, Homs N, de la Piscina PR, López Nieto JM. Evolution of the optimal catalytic systems for the oxidative dehydrogenation of ethane: The role of adsorption in the catalytic performance. J Catal 2022. [DOI: 10.1016/j.jcat.2021.07.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Li S, Lu Z, Yan Y, Deng L, Fan Y, Zhu N, Xu L, Yu S. The Structure and Catalytic Properties of MoVTeNbO Catalysts Modified by Adding Cr, Fe, Ce and W. CATALYSIS SURVEYS FROM ASIA 2021. [DOI: 10.1007/s10563-021-09346-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Abstract
Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.
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