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Lecture 7: Interatomic Potentials
Lec 43 Machine learned interatomic potentials hands on
Matlantis Webinar with MIT Professor Ju Li: Universal Machine Learning Interatomic Potential
Automating the composition of ML interatomic potentials in Julia | Emmanuel Lujan | JuliaCon 2023
Let's Talk Research Episode 3: Machine-learned interatomic potentials (MLIPs)
Jigyasa Nigam - Incorporating physical constraints and symmetry in atomic-scale machine learning
Daniel Schwalbe Koda: Machine learning for interatomic potentials
Gabor Csányi - Machine learning potentials: from polynomials to message passing networks
Nongnuch Artrith - Developing Artificial Neural Network Potentials for Materials
Symmetry and Uncertainty-aware Models of Interatomic Interactions for Fast Molecular Dynamics
Materials Project Seminars – Ju Li, A Universal Empirical Interatomic Potential
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Last Updated: September 28, 2026
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Summary
In this talk at NanoHUB's Hands-on Data Science and 2021.01.27 Yunxing Zuo, University of California, San Diego This video is part of NCN's Hands-on Data Science and IMA Data Science Seminar Speaker: Yangshuai Wang, University of British Columbia "Advancing This lecture covers an specific challenge with large importance to atomic-scale modeling: predicting the energy of a system of ... Message Passing Atomic Cluster Expansion, This is an edited recording of the Matlantis™ free webinar with Ju Li ( li.mit.edu/), a professor at the Department of Materials ... For more info on the Julia Programming Language, us on Twitter: twitter.com/JuliaLanguage and consider ... In Episode 3 of Let's Talk Research, we dive into the fast-evolving world of ... and symmetry in atomic-scale 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 ... Abstract: I will report on the recent Speaker: Boris KOZINSKY (Harvard University, USA) Young Researchers' Workshop on [J. Materiomics 9 (2023) 447] 0:00 Introduction 4:23
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