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physics.chem-ph2026★ 6 cited
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…
physics.chem-ph2025
The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
Mikołaj J. Gawkowski, Mingjia Li, Benjamin X. Shi +1
Training machine learning interatomic potentials (MLIPs) on total energies of molecular clusters using differential or transfer learning is becoming a popular route to extend the a…