3 papers
cs.LG2026
Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
Joanna Zou, Fraser Birks, Dallas Foster +1
Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data. We intr…
cond-mat.mtrl-sci2026
Resolving Structural Avalanches in Amorphous Carbon with Arclength Continuation
Fraser Birks, Ibrahim Ghanem, Lars Pastewka +2
Plastic deformation in amorphous solids is carried by localized shear transformations that self-organize into avalanches. In amorphous carbon modeled with a machine-learned interat…
cond-mat.mtrl-sci2025
Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
Fraser Birks, Matthew Nutter, Thomas D Swinburne +1
Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simu…