54:30
Anh Tran and Julien Tranchida - Multi-fidelity and parallel machine-learning approaches
52:30
Davide Tisi - Green-Kubo simulations of transport properties: from ab initio to neural networks
53:15
Bingqing Cheng - Predicting materials properties: from thermodynamics to transport
01:05:42
Yifan Li - From Deep Potential to DeePMD-kit, to DeepModeling
01:01:04
Edgar Engel - Simulations for bio-molecular systems made easy
01:04:16
Ganesh Sivaraman - From Atomistic to Coarse Grained
52:43
Christoph Schran - Machine learning potentials for complex aqueous systems made simple
58:47
Jigyasa Nigam - Incorporating physical constraints and symmetry in atomic-scale machine learning
59:20
Nataliya Lopanitsyna - Machine learning potential recipes on the example of metal alloys
56:29
Rose Cersonsky - Dimensionality Reduction for Machine Learning Representations
01:01:04
Berk Onat - Dimensionality of Atomic Environment Representations and Implanted Neural Networks
01:03:32
Emine Küçükbenli - Atomistic modelling with Neural Network Potentials with PANNA
59:46
Ivan Novikov - The MLIP package: Moment tensor potentials with active learning
49:19
Félix Musil - Building machine learned force fields with kernel methods: a hands-on tutorial
01:04:45
Nongnuch Artrith - Developing Artificial Neural Network Potentials for Materials
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About
ML-IP 2021 is a Psi-k tutorial workshop aimed at young and early-career researchers who are interested in using machine-learning interatomic potentials (ML-IP) in their work and would like to gain a solid theoretical and practical grounding in the field before embarking on a research project.
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