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Let's Talk Research Episode 3: Machine-learned interatomic potentials (MLIPs)
Daniel Schwalbe Koda: Machine learning for interatomic potentials
Adolphus Lye: Probabilistic Prediction of Material Properties with Artificial Intelligence (PROMAP)
Predicting Molecular Properties through Machine Learned Energy Functionals (Johannes Margraf)
Quantum Machine Learning: How Quantum Physics Meets AI
Physics AI for Digital Twins: Cutting Test Cycles and Program Risk in Aerospace & Defense
Machine Learning for Fluid Mechanics
Setting Up a Physics-Enhanced Machine Learning Study
Bingqing Cheng - Predicting materials properties with the help of ML | MLSS Kraków 2023
Machine learning accelerated design from molecules to materials
New Capabilities of Tensor Networks: Machine Learning and.. | Edwin Stoudenmire (Flatiron Institute)
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Last Updated: October 3, 2026
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Summary
This video provides an intro to molecular dynamics (MD) simulations, then goes into detail about the evolution of interatomic ... Porous materials and their mass transport Modeling of AMg6 aluminum alloy jump- deformation In Episode 3 of Let's Talk Research, we dive into the 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 ... In this talk, Adolphus Lye will discuss his recent work on Project PROMAP which was a feasibility study funded by the Advanced ... In this talk I will present recent work on In Aerospace and Defense, a valuable digital twin of an aircraft or missile system has three key capabilities - predict the complex ... eigensteve on Twitter This video gives an overview of how Prof. Heather Kulik MIT, Cambridge, MA, USA Löwdin Lecturer 2023 Abstract Full title: New Capabilities of Tensor Networks:
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