Media Summary: Join Portal to connect with the speakers: This is a recording from the 2024 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 ... QISCA Journal Club 2026 winter break - January 26th Presentation by Seungbin Gweon(권승빈), KHUantum Title:

Day 2 Learning Ml Interatomic - Detailed Analysis & Overview

Join Portal to connect with the speakers: This is a recording from the 2024 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 ... QISCA Journal Club 2026 winter break - January 26th Presentation by Seungbin Gweon(권승빈), KHUantum Title: Lennard-Jones Centre discussion group seminar by Filippo Bigi from EPFL in Switzerland . Machine-learned Introduction to Molecular Representations and Featurization by Dr. Yamil Colon, University of Notre Dame This lecture is part of ... This video provides an intro to molecular dynamics (MD) simulations, then goes into detail about the evolution of

Perform basic molecular dynamics/static simulations with universal Message Passing Atomic Cluster Expansion, Machine Learned 2021.01.27 Yunxing Zuo, University of California, San Diego This video is part of NCN's Hands-on Data Science and This is an edited recording of the Matlantis™ free webinar with Ju Li ( a professor at the Department of Materials ...

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Day 2 - Learning ML Interatomic Potentials | Gianni De Fabritiis
Daniel Schwalbe Koda: Machine learning for interatomic potentials
[JC] Machine Learning Interatomic Potentials
Beyond Interatomic Potentials - Further Acceleration of Atomic-Scale SImulations
Day 2 Part 2 Molecular Featurization
Day 2 Part 1: Molecular Featurization Intro
Day 2
Machine Learning Meets Molecular Dynamics: A Crash Course in MLIPs for Solids
Platonic Representation of Foundation ML Interatomic Potentials I LeMaterial Reading Group
Use Universal Machine-Learning Interatomic Potential (MACE) in Interactive Interface
Lec 43 Machine learned interatomic potentials hands on
Convenient and efficient development of Machine Learning Interatomic Potentials
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Day 2 - Learning ML Interatomic Potentials | Gianni De Fabritiis

Day 2 - Learning ML Interatomic Potentials | Gianni De Fabritiis

Join Portal to connect with the speakers: https://portal.valencelabs.com/ This is a recording from the 2024

Daniel Schwalbe Koda: Machine learning for interatomic potentials

Daniel Schwalbe Koda: Machine learning for interatomic potentials

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 ...

[JC] Machine Learning Interatomic Potentials

[JC] Machine Learning Interatomic Potentials

QISCA Journal Club 2026 winter break - January 26th Presentation by Seungbin Gweon(권승빈), KHUantum Title:

Beyond Interatomic Potentials - Further Acceleration of Atomic-Scale SImulations

Beyond Interatomic Potentials - Further Acceleration of Atomic-Scale SImulations

Lennard-Jones Centre discussion group seminar by Filippo Bigi from EPFL in Switzerland . Machine-learned

Day 2 Part 2 Molecular Featurization

Day 2 Part 2 Molecular Featurization

Introduction to Molecular Representations and Featurization by Dr. Yamil Colon, University of Notre Dame This lecture is part of ...

Day 2 Part 1: Molecular Featurization Intro

Day 2 Part 1: Molecular Featurization Intro

Introduction to Molecular Representations and Featurization by Dr. Yamil Colon, University of Notre Dame This lecture is part of ...

Day 2

Day 2

For details see https://www.cecam.org/workshop-details/school-on-

Machine Learning Meets Molecular Dynamics: A Crash Course in MLIPs for Solids

Machine Learning Meets Molecular Dynamics: A Crash Course in MLIPs for Solids

This video provides an intro to molecular dynamics (MD) simulations, then goes into detail about the evolution of

Platonic Representation of Foundation ML Interatomic Potentials I LeMaterial Reading Group

Platonic Representation of Foundation ML Interatomic Potentials I LeMaterial Reading Group

Paper Link: https://www.nature.com/articles/s42256-026-01235-7 Foundation

Use Universal Machine-Learning Interatomic Potential (MACE) in Interactive Interface

Use Universal Machine-Learning Interatomic Potential (MACE) in Interactive Interface

Perform basic molecular dynamics/static simulations with universal

Lec 43 Machine learned interatomic potentials hands on

Lec 43 Machine learned interatomic potentials hands on

Message Passing Atomic Cluster Expansion, Machine Learned

Convenient and efficient development of Machine Learning Interatomic Potentials

Convenient and efficient development of Machine Learning Interatomic Potentials

2021.01.27 Yunxing Zuo, University of California, San Diego This video is part of NCN's Hands-on Data Science and

Matlantis Webinar with MIT Professor Ju Li: Universal Machine Learning Interatomic Potential

Matlantis Webinar with MIT Professor Ju Li: Universal Machine Learning Interatomic Potential

This is an edited recording of the Matlantis™ free webinar with Ju Li (http://li.mit.edu/), a professor at the Department of Materials ...