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[JC] Machine Learning Interatomic Potentials
Quantum Information Science Club Association
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Daniel Schwalbe Koda: Machine learning for interatomic potentials
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Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA
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Gabor Csányi - Machine learning potentials: from polynomials to message passing networks
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Machine Learning Meets Molecular Dynamics: A Crash Course in MLIPs for Solids
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Christoph Schran - Machine learning potentials for complex aqueous systems made simple
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Materials Project Seminars – Ju Li, "A Universal Empirical Interatomic Potential"
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Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA
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Energy-Based Transformers explained | How EBTs and EBMs work
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Atomistic Simulations with High-Dimensional Neural Network Potentials by Jörg Behler bit.ly/3MiHcJ8
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Andrew Christlieb - An introduction to Scientific Machine Learning - IPAM at UCLA
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