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Machine learning potentials always extrapolate, it does not matter
Cambridge Materials
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Neural networks that think!: Extrapolating and interpreting in scientific deep learning
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Daniel Schwalbe Koda: Machine learning for interatomic potentials
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Stefan Chmiela - Non-locality in machine learning force fields - IPAM at UCLA
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Extrapolation with machine learning based early-stage energy prediction models
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Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA
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View & Download![[Clip] 061 Interpolation, Extrapolation and Linearisation Prof Yann LeCun, Dr Randall Balestriero — StackSmasher Ninja — machine learning potentials always extrapolate it does not matter YouTube to MP3 & MP4 download on TubeGalore](/api/image/O9bGnalUJjk/mqdefault)
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![AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning] — Steve Brunton — machine learning potentials always extrapolate it does not matter YouTube to MP3 & MP4 download on TubeGalore](/api/image/GCz6afDVy5Y/mqdefault)
24:01
AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]
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Christoph Schran - Machine learning potentials for complex aqueous systems made simple
mlip-workshop-2021
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26:49
Thermodynamic properties by on-the-fly machine-learned potentials within and beyond DFT
Cambridge Materials
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