Active learning in Bayesian neural networks for the bandgap predictions of a large number of novel Van der Waals heterostructures

Marco Fronzi, Olexandr Isayev, David A. Winkler, Joseph George Shapter, Amanda Ellis, Peter C. Sherrell, Nick A. Shepelin, Alexander Corletto, Mike Ford

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The bandgap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify materials suitable for device applications, is very computationally expensive. Here, active machine learning algorithms are used to leverage a limited number of accurate density functional theory calculations to robustly predict the bandgap of a very large number of novel 2D heterostructures. Using this approach, a database of ≈2.2 million bandgap values for various novel 2D van der Waals heterostructures is produced.
Original languageEnglish
Article number2100080
Number of pages7
JournalAdvanced Intelligent Systems
Volume3
Issue number11
DOIs
Publication statusPublished - Nov 2021

Keywords

  • active learning, Bayesian methods, 2D materials, bandgap, neural; network

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