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Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
Computational chemistry for water-splitting electrocatalysis
L Miao, W Jia, X Cao, L Jiao - Chemical Society Reviews, 2024 - pubs.rsc.org
Electrocatalytic water splitting driven by renewable electricity has attracted great interest in
recent years for producing hydrogen with high-purity. However, the practical applications of …
recent years for producing hydrogen with high-purity. However, the practical applications of …
Machine learning for perovskite solar cells and component materials: key technologies and prospects
Data‐driven epoch, the development of machine learning (ML) in materials and device
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …
[HTML][HTML] Mechanistic models for additive manufacturing of metallic components
Additive manufacturing (AM), also known as 3D printing, is gaining wide acceptance in
diverse industries for the manufacturing of metallic components. The microstructure and …
diverse industries for the manufacturing of metallic components. The microstructure and …
Machine learning in materials science
Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …
method and the density functional theory (DFT)‐based method, are unable to keep pace …
Data‐driven materials science: status, challenges, and perspectives
Data‐driven science is heralded as a new paradigm in materials science. In this field, data is
the new resource, and knowledge is extracted from materials datasets that are too big or …
the new resource, and knowledge is extracted from materials datasets that are too big or …
Strategies for improving the sustainability of structural metals
Metallic materials have enabled technological progress over thousands of years. The
accelerated demand for structural (that is, load-bearing) alloys in key sectors such as …
accelerated demand for structural (that is, load-bearing) alloys in key sectors such as …
Data‐driven materials innovation and applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
[HTML][HTML] DAMASK–The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single …
Crystal Plasticity (CP) modeling is a powerful and well established computational materials
science tool to investigate mechanical structure–property relations in crystalline materials. It …
science tool to investigate mechanical structure–property relations in crystalline materials. It …
A strategy to apply machine learning to small datasets in materials science
There is growing interest in applying machine learning techniques in the research of
materials science. However, although it is recognized that materials datasets are typically …
materials science. However, although it is recognized that materials datasets are typically …