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Abstract
This paper introduces a machine learning approach to predict the vibrational stability of materials, a crucial factor in material synthesizability. Using a dataset of approximately 3100 materials, the authors trained a classifier to distinguish between vibrationally stable and unstable materials. This classifier offers a significantly faster alternative to computationally expensive first-principles calculations, potentially serving as a valuable filtering tool for online material databases.
Publisher
npj Computational Materials
Published On
Jan 11, 2023
Authors
Sherif Abdulkader Tawfik, Mahad Rashid, Sunil Gupta, Salvy P. Russo, Tiffany R. Walsh, Svetha Venkatesh
Tags
machine learning
vibrational stability
materials
classifier
synthesizability
computational methods

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