TY  - JOUR
AU  - Malek, Ali
AU  - Eslamibidgoli, Mohammad Javad
AU  - Mokhtari, Mehrdad
AU  - Wang, Qianpu
AU  - Eikerling, Michael H.
AU  - Malek, Kourosh
TI  - Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts
JO  - ChemPhysChem
VL  - 20
IS  - 22
SN  - 1439-7641
CY  - Weinheim
PB  - Wiley-VCH Verl.
M1  - FZJ-2020-00388
SP  - 2946 - 2955
PY  - 2019
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:31587461
UR  - <Go to ISI:>//WOS:000501279800005
DO  - DOI:10.1002/cphc.201900570
UR  - https://juser.fz-juelich.de/record/872923
ER  -