3 papers
cond-mat.str-el2025
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…
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.…
cs.LG2024
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…