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| Home > Publications database > Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts |
| Home > Publications database > Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts |
| Journal Article | FZJ-2020-00388 |
; ; ; ; ;
2019
Wiley-VCH Verl.
Weinheim
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Please use a persistent id in citations: doi:10.1002/cphc.201900570
Abstract: Similar to 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 expected 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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