226 citations · 414 across the 16 of their papers we have counts for
16 papers
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.…
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics
Michael Moran, Vladimir V. Gusev, Michael W. Gaultois +2
The scarcity of property labels remains a key challenge in materials informatics, whereas materials data without property labels are abundant in comparison. By pretraining supervis…
A Fermi Surface Descriptor Quantifying the Correlations between Anomalous Hall Effect and Fermi Surface Geometry
Elena Derunova, Jacob Gayles, Yan Sun +2
In the last few decades, basic ideas of topology have completely transformed the prediction of quantum transport phenomena. Following this trend, we go deeper into the incorporatio…
Metrics for quantifying isotropy in high dimensional unsupervised clustering tasks in a materials context
Samantha Durdy, Michael W. Gaultois, Vladimir Gusev +2
Clustering is a common task in machine learning, but clusters of unlabelled data can be hard to quantify. The application of clustering algorithms in chemistry is often dependant o…
Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials
Andrij Vasylenko, Benjamin Asher, Chris C. Collins +4
Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches…
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…