38 citations · 59 across the 10 of their papers we have counts for
6 papers · 1 filter
A fast summation method for the DFT-D3 dispersion correction
Victoria Valeeva, Cheuk Hin Ho, Mario Geiger +4
The DFT-D3 dispersion correction is routinely added to machine learning force fields (MLFFs) trained on dispersion-deficient functionals such as PBE. Its environment-dependent pair…
Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials
Cheuk Hin Ho, Cas van der Oord, James P. Darby +11
Magnetism governs key properties of materials used in energy, data storage, and spintronic technologies, yet its complex coupling to lattice and electronic degrees of freedom chall…
False Metallization in Short-Ranged Machine Learned Interatomic Potentials
Isaac J. Parker, Mandy J. Hoffmann, William J. Baldwin +7
Machine learned interatomic potentials (MLIPs) have enabled atomistic simulations with ab initio accuracy for a fraction of the computational cost. However, many widely used MLIPs…
Regularity Priors for the Linear Atomic Cluster Expansion
James P. Darby, Joe D. Morrow, Albert P. Bartók +3
Machine-learned interatomic potentials enable large systems to be simulated for long time scales at near ab-initio accuracy. This accuracy is achieved by fitting extremely flexible…
MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
Ilyes Batatia, William J. Baldwin, Domantas Kuryla +10
Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on loc…
Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs
Thomas Warford, Fabian L. Thiemann, Gábor Csányi
The training of foundational machine learning interatomic potentials (fMLIPs) relies on diverse databases with energies and forces calculated using ab initio methods. We show that…