420 citations · 715 across the 9 of their papers we have counts for
9 papers
Crystal Structure Generation with Autoregressive Large Language Modeling
Luis M. Antunes, Keith T. Butler, Ricardo Grau-Crespo
The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. Quickly generating an…
Predicting Thermoelectric Transport Properties from Composition with Attention-based Deep Learning
Luis M. Antunes, Keith T. Butler, Ricardo Grau-Crespo
Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even to…
Unified Graph Neural Network Force-field for the Periodic Table
Kamal Choudhary, Brian DeCost, Lily Major +3
Classical force fields (FF) based on machine learning (ML) methods show great potential for large scale simulations of materials. MLFFs have hitherto largely been designed and fitt…
Versatile Domain Mapping Of Scanning Electron Nanobeam Diffraction Datasets Utilising Variational AutoEncoders and Decoder-Assisted Latent-Space Clustering
Andy Bridger, William I. F. David, Thomas J. Wood +2
Advancements in fast electron detectors have enabled the statistically significant sampling of crystal structures on the nanometre scale by means of Scanning Electron Nanobeam Diff…
Interpretable and Explainable Machine Learning for Materials Science and Chemistry
Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi +1
While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successf…
Entropy-based Active Learning of Graph Neural Network Surrogate Models for Materials Properties
Johannes Allotey, Keith T. Butler, Jeyan Thiyagalingam
Graph neural networks, trained on experimental or calculated data are becoming an increasingly important tool in computational materials science. Networks, once trained, are able t…
Determining the maximum information gain and optimising experimental design in neutron reflectometry using the Fisher information
James H. Durant, Lucas Wilkins, Keith Butler +1
An approach based on the Fisher information (FI) is developed to quantify the maximum information gain and optimal experimental design in neutron reflectometry experiments. In thes…
Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data
Keith T. Butler, Manh Duc Le, Jeyarajan Thiyagalingam +1
Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' pe…
Machine Learning and Big Scientific Data
Tony Hey, Keith Butler, Sam Jackson +1
This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the…