3 papers
cond-mat.mtrl-sci2024
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
Federico Ottomano, John Y. Goulermas, Vladimir Gusev +14
Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases.…
cond-mat.mtrl-sci2024
Learning Atoms from Crystal Structure
Andrij Vasylenko, Dmytro Antypov, Sven Schewe +4
Computational modelling of materials using machine learning, ML, and historical data has become integral to materials research. The efficiency of computational modelling is strongl…
cond-mat.mtrl-sci2023
Epitaxial growth, optical and electrical conductivity of the metallic pyrochlore BiRuO on Y-stabilized ZrO substrate
Marita O'Sullivan, Jonathan Alaria, Mike W. Gaultois +3
We report on the epitaxial growth, structural and electrical properties of metallic pyrochlore bismuth ruthenate heterostructures grown along both the [001] and [111] directions. O…