120 citations · 223 across the 24 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…