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Daniel Schwalbe Koda: Machine learning for interatomic potentials
Machine Learning Interatomic Potential Development with MAML
Lec 43 Machine learned interatomic potentials hands on
Convenient and efficient development of Machine Learning Interatomic Potentials
Gabor Csányi - Machine learning potentials: from polynomials to message passing networks
Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA
Christoph Schran - Machine learning potentials for complex aqueous systems made simple
Automating the composition of ML interatomic potentials in Julia | Emmanuel Lujan | JuliaCon 2023
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA
Machine Learned Interatomic Potentials
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Last Updated: August 17, 2026
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