226 citations · 414 across the 16 of their papers we have counts for
3 papers · 1 filter
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…
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…
Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties
Samantha Durdy, Michael Gaultois, Vladimir Gusev +2
With machine learning being a popular topic in current computational materials science literature, creating representations for compounds has become common place. These representat…