38 citations · 59 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Random Spin Committee Approach For Smooth Interatomic Potentials
Vlad Cărare, Volker L. Deringer, Gábor Csányi
Training interatomic potentials for spin-polarized systems continues to be a difficult task for the molecular modeling community. In this note, a proof-of-concept, random initial s…
Transferability of datasets between Machine-Learning Interaction Potentials
Samuel P. Niblett, Panagiotis Kourtis, Ioan-Bogdan Magdău +2
With the emergence of Foundational Machine Learning Interatomic Potential (FMLIP) models trained on extensive datasets, transferring data between different ML architectures has bec…