781 citations · 1.6k across the 27 of their papers we have counts for
30 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls
Katharine Fisher, Michael Herbst, James Kermode +1
Machine learning has become the standard tool for the design of interatomic potentials which balance efficiency and accuracy, but uncertainty quantification remains an open problem…
Accurate and Efficient Interatomic Potentials for Dislocations in InP
Thomas Rocke, Thomas Hudson, Richard Beanland +1
We present Atomic Cluster Expansion (ACE) and MACE models trained on a new dataset of Density Functional Theory (DFT) calculations, constructed for the task of studying the mobilit…
Bridging Atomistic and Continuum Descriptions of Nanoscale Dislocation Loops in Tungsten
Joseph Duque Lopez, Sergei Dudarev, James Kermode +1
In order to predict the long-term effects of irradiation on the material properties of tungsten, a continuum approach to simulating the interactions of dislocation loops, which ari…
Multilevel radial basis function surrogates for noise-robust DSMC-CFD coupling
Arshad Kamal, Arun K. Chinnappan, James R. Kermode +1
Hybrid methods for simulating rarefied gas flows reduce computational cost by coupling a particle-based model, typically the direct simulation Monte Carlo (DSMC) method, to a conti…
Bayesian Selection for Efficient MLIP Dataset Selection
Thomas Rocke, James Kermode
The problem of constructing a dataset for MLIP development which gives the maximum quality in the minimum amount of compute time is complex, and can be approached in a number of wa…