1:27:06
Daniel Schwalbe Koda: Machine learning for interatomic potentials
FAIRmat and NOMAD
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33:03
Daniel Schwalbe-Koda | Adversarial Sampling and Extrapolation Trends in NN Potentials | Lecture
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58:38
Gabor Csányi - Machine learning potentials: from polynomials to message passing networks
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42:54
Daniel Schwalbe-Koda | Adversarial Sampling and Extrapolation Trends in NN Potentials | Tutorial
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Christoph Schran - Machine learning potentials for complex aqueous systems made simple
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47:15
Convenient and efficient development of Machine Learning Interatomic Potentials
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Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA
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Machine Learning Meets Molecular Dynamics: A Crash Course in MLIPs for Solids
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59:46
Ivan Novikov - The MLIP package: Moment tensor potentials with active learning
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46:25
Daniel Schwalbe-Koda - Scales in Material Modeling w/ Atomistic Sim, Info Theory, & Generative Model
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1:40:55
MMM Hub Software Spotlight: Machine Learning (ML) force fields
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"Representing atoms clouds: the foundations of atomic-scale machine learning" Prof. Michele Ceriotti
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19:19
Machine learning potentials always extrapolate, it does not matter
Cambridge Materials
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