9 citations · 14 across the 4 of their papers we have counts for
5 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…
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…
Comment on "Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four body interactions"
Sergey N. Pozdnyakov, Michael J. Willatt, Albert P. Bartók +3
The "quasi-constant" SOAP and ACSF fingerprint manifolds recently discovered by Parsaeifard and Goedecker are a direct consequence of the presence of degenerate pairs of configurat…