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Grain Boundary Database materialsvirtuallab.org/2020/01/grain-…

Performance and Cost of Machine Learning Interatomic Potentials materialsvirtuallab.org/2020/01/perfor…

Critical Review of ML in Energy Materials materialsvirtuallab.org/2020/01/critic…

Chemical Reviews on Rechargeable Alkali-Ion Battery Materials: Theory and Computation materialsvirtuallab.org/2020/02/chemic…

Genetic algorithm-guided deep learning of grain boundary diagrams materialsvirtuallab.org/2020/04/geneti…

Random Forest Models for Coordination Environment from XANES materialsvirtuallab.org/2020/04/random…

Complex Strengthening Mechanisms in Multi-Principal Element Alloys, aka High-Entropy Alloys materialsvirtuallab.org/2020/06/comple…

Nature Energy article on Ultrafast Ion Transport at a Cathode–Electrolyte Interface materialsvirtuallab.org/2020/06/nature…

Predicting Thermal Quenching in Phosphors materialsvirtuallab.org/2020/07/predic…

Design Principles for Aqueous Na-Ion Battery Cathodes materialsvirtuallab.org/2020/08/design…

Disordered Rock Salt Anode for Fast-charging Lithium-ion Batteries materialsvirtuallab.org/2020/09/disord…

Lithium-rich disordered rocksalt metal oxide cathodes have long been investigated as battery cathodes. Research published in Nature shows that they also have potential as an anode material for fast-charging batteries. go.nature.com/3bnqZzX

ASTAR Webinar on The AI Revolution in Materials Science materialsvirtuallab.org/2020/09/astar-…

Joule & Energy and AI Symposium materialsvirtuallab.org/2020/09/joule-…

High-entropy Na-ion cathode materialsvirtuallab.org/2020/12/high-e…

High-entropy Na-ion cathodes exhibit low diffusion barriers and high rate capability. materialsvirtuallab.org/2020/12/high-e…

Cation mixing in Na3PnS4-Na4TtS4 results in worst of both worlds for electrochemical stability but can improve ionic conductivity and moisture stability. materialsvirtuallab.org/2021/01/design…

New research from UC San Diego nanoengineers included in the 1st edition of Nature Computational Science! The @NANO_UCSD Materials Virtual Lab showed they can accurately predict the properties of materials (including disordered materials) using a mix of low & high quality data ucsdnews.ucsd.edu/pressrelease/n…

Better predictions on valuable materials properties can be made by using diverse data sources. nature.com/articles/d4158…

We are thrilled to welcome our new Associate Editor, Prof. Shyue Ping Ong Materials Virtual Lab UCSD Engineering! His group focuses on data-driven materials design, first principles calculations, materials informatics, machine learning, and artificial intelligence: go.acs.org/gr