Browsing College of Engineering (MU) by Identifier "10.1038/s41524-019-0165-4"
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Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride
(Nature Publishing Group, 2019)It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials. However, predicting such effects is challenging due to the large ...