186 citations · 829 across the 29 of their papers we have counts for
32 papers
Probabilistic selection and design of concrete using machine learning
Jessica C. Forsdyke, Bahdan Zviazhynski, Janet M. Lees +1
Development of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and a multitude of possible combinations o…
Machine learning superalloy microchemistry and creep strength from physical descriptors
Patrick L Taylor, Gareth Conduit
We propose an element-agnostic set of descriptors to model superalloy properties with Gaussian process regression. Furthermore, we develop a correction method to deliver the best a…
Unveil the unseen: Exploit information hidden in noise
Bahdan Zviazhynski, Gareth Conduit
Noise and uncertainty are usually the enemy of machine learning, noise in training data leads to uncertainty and inaccuracy in the predictions. However, we develop a machine learni…
Machine learning predictions of superalloy microstructure
Patrick L. Taylor, Gareth Conduit
Gaussian process regression machine learning with a physically-informed kernel is used to model the phase compositions of nickel-base superalloys. The model delivers good predictio…
Temporal fluctuation induced order in conventional superconductors
Darryl Foo, Gareth Conduit
Communal pairing in superconductors introduces variational freedom for Cooper pairs to share fermions. Temporal oscillations of the superconducting gap entropically drive communal…
OPTIMADE, an API for exchanging materials data
Casper W. Andersen, Rickard Armiento, Evgeny Blokhin +53
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible an…