Similar to the advancements gained from big data in genomics, security, internet of things, and e-commerce, the materials workflow could be made more efficient and prolific through advances in streamlining data sources, autonomous materials synthesis, rapid characterization, big data analytics, and self-learning algorithms. In electrochemical materials science, data sets are large, unstructured/heterogeneous, and difficult to process and analyze from a single data channel or platform. Computer-aided materials design together with advances in data mining, machine learning, and predictive analytics are touted to provide inexpensive and accelerated pathways towards tailor-made functionally optimized energy materials. Fundamental research in the field of electrochemical energy materials focuses primarily on complex interfacial phenomena and kinetic electrocatalytic processes. This perspective article critically assesses AI-driven modeling and computational approaches that are currently applied to those objects. An application-driven materials intelligence platform is introduced, and its functionalities are scrutinized considering the development of electrocatalyst materials for CO2 conversion as a use case.
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Malek, A. , Eslamibidgoli, M. J. , Mokhtari, M. , Wang, Q. , Eikerling, M. H. and Malek, K. (2019), Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts. ChemPhysChem. DOI:10.1002/cphc.201900570.
Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts
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