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Let's Talk Research Episode 3: Machine-learned interatomic potentials (MLIPs)
Lec 40 Introduction to machine learned potentials
Lecture 7: Interatomic Potentials
Dr. Volker Deringer (Oxford) --- Machine-learned interatomic potentials for materials chemistry
Daniel Schwalbe Koda: Machine learning for interatomic potentials
Machine Learning for Mathematicians | Lecture 8: Cross-Entropy, Why the Fused Gradient Is p Minus y
Machine Learning for Mathematicians | Lecture 9: Losses Are Negative Log-Likelihoods
ICML 2024 TutorialMachine Learning on Function spaces #NeuralOperators
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Last Updated: September 30, 2026
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Graph neural network, message passing, invariance, equivarience, spherical harmonics. Message Passing Atomic Cluster Expansion, This video provides an intro to molecular dynamics (MD) simulations, then goes into detail about the evolution of QISCA Journal Club 2026 winter break - January 26th Presentation by Seungbin Gweon(권승빈), KHUantum Title: In Episode 3 of Let's Talk Research, we dive into the fast-evolving world of 12 February, 2026 15:00 (local Swedish time) This video was recorded as part of the 4th IKZ - FAIRmat winter school, a hybrid event, online and on-site in Berlin, January 23 -25 ... The softmax Jacobian is a k by k matrix. Compose it with one more function and the whole thing collapses to a vector subtraction. The one half in front of a squared error is a variance. Drop it and you have changed what you believe about the noise. Every loss ...
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