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Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary, Brian DeCost, Chi Chen +10
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data mo…
Scale-invariant Machine-learning Model Accelerates the Discovery of Quaternary Chalcogenides with Ultralow Lattice Thermal Conductivity
Koushik Pal, Cheol Woo Park, Yi Xia +2
Intrinsically low lattice thermal conductivity () is a desired requirement in many crystalline solids such as thermal barrier coatings and thermoelectrics. Here, we design an…
Exploring low lattice thermal conductivity materials using chemical bonding principles
Jiangang He, Yi Xia, Wenwen Lin +4
Semiconductors with very low lattice thermal conductivities are highly desired for applications relevant to thermal energy conversion and management, such as thermoelectrics and th…
OPTIMADE, an API for exchanging materials data
Casper W. Andersen, Rickard Armiento, Evgeny Blokhin +53
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible an…