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 Machine Learning Training Series which can be found at: https://nanohub.org/groups/ml/handsontraining
This tutorial introduces the concepts of machine learning interatomic potentials (ML-IAPs) in materials science, including two components of local environment atomic descriptors and machine learning models. Using the prepared dataset, you will learn how to build a prototype ML-IAP and use it to predict basic material properties for a multi-component system.
The nanoHUB tool "maml: Machine Learning Force Field for Materials" used in this hands-on tutorial can be found at: https://nanohub.org/tools/maml
This talk and additional downloads can be found on nanoHUB.org at: https://nanohub.org/resources/34745
Видео Convenient and efficient development of Machine Learning Interatomic Potentials канала nanohubtechtalks
This video is part of NCN's Hands-on Data Science and Machine Learning Training Series which can be found at: https://nanohub.org/groups/ml/handsontraining
This tutorial introduces the concepts of machine learning interatomic potentials (ML-IAPs) in materials science, including two components of local environment atomic descriptors and machine learning models. Using the prepared dataset, you will learn how to build a prototype ML-IAP and use it to predict basic material properties for a multi-component system.
The nanoHUB tool "maml: Machine Learning Force Field for Materials" used in this hands-on tutorial can be found at: https://nanohub.org/tools/maml
This talk and additional downloads can be found on nanoHUB.org at: https://nanohub.org/resources/34745
Видео Convenient and efficient development of Machine Learning Interatomic Potentials канала nanohubtechtalks
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