33:08
Interatomic potentials from first principles
ICTP Condensed Matter and Statistical Physics
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47:15
Convenient and efficient development of Machine Learning Interatomic Potentials
nanohubtechtalks
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Daniel Schwalbe Koda: Machine learning for interatomic potentials
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Machine Learning Meets Molecular Dynamics: A Crash Course in MLIPs for Solids
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Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA
Institute for Pure & Applied Mathematics (IPAM)
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nanoHUB-U Atoms to Materials L3.2: Interatomic Potentials for Molecular Materials
nanohubtechtalks
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Learning Uniformly Accurate Interatomic Potentials from Scratch
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12 Interatomic potentials for classical molecular dynamics and Monte Carlo simulations
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nanoHUB-U Atoms to Materials L3.3: Interatomic Potentials for Molecular Materials
nanohubtechtalks
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Gabor Csányi - Machine learning potentials: from polynomials to message passing networks
Institute for Pure & Applied Mathematics (IPAM)
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Karsten Reuter - First-Principle based Modelling of Electrocatalysis Beyond Potential of Zero Charge
Institute for Pure & Applied Mathematics (IPAM)
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Molecular Dynamics with Neural Equivariant Interatomic Potentials
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41:23
Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA
Institute for Pure & Applied Mathematics (IPAM)
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A Unified Understanding of Equivariant Interatomic Potentials
ICTP Condensed Matter and Statistical Physics
832 views
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30:15