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 Handson Data Science and Machine Learning Training Series which can be found at: This tutorial introduces the concepts of machine learning interatomic potentials (MLIAPs) 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 MLIAP and use it to predict basic material properties for a multicomponent system. The nanoHUB tool maml: Machine Learning Force Field for Materials used in this handson tutorial can be found at: This talk and additional downloads can be found on at: