2 citations · 2 across the 1 of their papers we have counts for
3 papers · 1 filter
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…
How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets
Domantas Kuryla, Fabian Berger, Gábor Csányi +1
Training of general-purpose machine learning interatomic potentials (MLIPs) relies on large datasets with properties usually computed with density functional theory (DFT). A pre-re…
Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water
Niamh O'Neill, Benjamin X. Shi, William Baldwin +5
Simulating liquid water to an accuracy that matches its wealth of available experimental data requires both precise electronic structure methods and reliable sampling of nuclear (q…