2 citations · 5 across the 4 of their papers we have counts for
5 papers
Global properties of the energy landscape: a testing and training arena for machine learned potentials
Vlad Cărare, Fabian L. Thiemann, Joe Morrow +3
Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics -- critical for reaction…
Refining embeddings with fill-tuning: data-efficient generalised performance improvements for materials foundation models
Matthew P. Wilson, Edward O. Pyzer-Knapp, Nicolas Galichet +1
Pretrained foundation models learn embeddings that can be used for a wide range of downstream tasks. These embeddings optimise general performance, and if insufficiently accurate a…
Evolution of -means solution landscapes with the addition of dataset outliers and a robust clustering comparison measure for their analysis
Luke Dicks, David J. Wales
The -means algorithm remains one of the most widely-used clustering methods due to its simplicity and general utility. The performance of -means depends upon location of mini…
Physics Inspired Approaches To Understanding Gaussian Processes
Maximilian P. Niroomand, Luke Dicks, Edward O. Pyzer-Knapp +1
Prior beliefs about the latent function to shape inductive biases can be incorporated into a Gaussian Process (GP) via the kernel. However, beyond kernel choices, the decision-maki…
Archetypal solution spaces for clustering gene expression datasets in identification of cancer subtypes
Yuchen Wu, Luke Dicks, David J. Wales
Gene expression profiles are essential in identifying different cancer phenotypes. Clustering gene expression datasets can provide accurate identification of cancerous cell lines,…