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Beyond Interatomic Potentials - Further Acceleration of Atomic-Scale SImulations
Daniel Schwalbe Koda: Machine learning for interatomic potentials
Interatomic forcefield parameterization by active learning
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
nanoHUB-U Atoms to Materials L5.4: Reactive Interatomic Potentials
Ralf Drautz - From electrons to the simulation of materials - IPAM at UCLA
Atomic Cluster Expansion: A framework for fast and accurate ML force fields
Gabor Csányi - Machine learning potentials: from polynomials to message passing networks
nanoHUB-U Atoms to Materials L3.3: Interatomic Potentials for Molecular Materials
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Last Updated: August 16, 2026
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