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Convenient and efficient development of Machine Learning Interatomic Potentials
nanohubtechtalks
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Daniel Schwalbe Koda: Machine learning for interatomic potentials
FAIRmat and NOMAD
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Automating the composition of ML interatomic potentials in Julia | Emmanuel Lujan | JuliaCon 2023
The Julia Programming Language
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56:27
Machine Learning Meets Molecular Dynamics: A Crash Course in MLIPs for Solids
Atoms to Materials
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58:38
Gabor Csányi - Machine learning potentials: from polynomials to message passing networks
Institute for Pure & Applied Mathematics (IPAM)
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52:43
Christoph Schran - Machine learning potentials for complex aqueous systems made simple
mlip-workshop-2021
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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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Emine Küçükbenli - Atomistic modelling with Neural Network Potentials with PANNA
mlip-workshop-2021
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16:03
Molecular Dynamics with Neural Equivariant Interatomic Potentials
Materials Intelligence Research
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