9 citations · 14 across the 4 of their papers we have counts for
3 papers · 1 filter
Optimal data generation for machine learned interatomic potentials
Connor Allen, Albert P. Bartók
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious…
Massively Parallel Fitting of Gaussian Approximation Potentials
Sascha Klawohn, James R. Kermode, Albert P. Bartók
We present a data-parallel software package for fitting Gaussian Approximation Potentials (GAPs) on multiple nodes using the ScaLAPACK library with MPI and OpenMP. Until now the ma…
Uncertainty quantification design principles for machine learning interatomic potentials: lessons learned from hierarchical Bayesian inference
Albert P. Bartók, Brennon L. Shanks, James. R. Kermode +1
Reliable uncertainty quantification (UQ) remains a major challenge for machine learning interatomic potentials (MLIPs). Here, we benchmark UQ strategies for message passing neural…