High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

Abstract With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional ap...

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Bibliographic Details
Main Authors: Andrew S. Rosen, Victor Fung, Patrick Huck, Cody T. O’Donnell, Matthew K. Horton, Donald G. Truhlar, Kristin A. Persson, Justin M. Notestein, Randall Q. Snurr
Format: Article
Language:English
Published: Nature Portfolio 2022-05-01
Series:npj Computational Materials
Online Access:https://doi.org/10.1038/s41524-022-00796-6

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https://doi.org/10.1038/s41524-022-00796-6

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