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: 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: This talk and additional downloads can be found on at: