Journal article
Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
Marco Fronzi, Olexandr Isayev, David A Winkler, Joseph G Shapter, Amanda Ellis, Peter C Sherrell, Nick A Shepelin, Alexander Corletto, Michael J Ford
ADVANCED INTELLIGENT SYSTEMS | WILEY | Published : 2021
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.
Related Projects (1)
Grants
Awarded by Australian Government through the Australian Research Council
Awarded by NSF
Funding Acknowledgements
The authors gratefully acknowledge the financial support of Australian Government through the Australian Research Council (ARC DP200101217). The theoretical calculations in this research were undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. The theoretical calculations in this work were also supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. O.I. acknowledges support from NSF CHE-1802789 and CHE-2041108. This work was carried out, in part, at the Centre for Integrated Nanotechnologies, an Office of Science User Facility operated for the USA. Department of Energy (DOE) Office of Science.