English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The atomic environments of semiconductor heterostructures can be highlyvaried as various structural imperfections, lattice mismatch and non-uniformstrain environments are generally present. The computational costs offirst-principles modeling techniques make it challenging to fully explore howatomic environments tune the electronic bands of heterostructures. We present amachine learning (ML)-assisted first-principles modeling framework thatestablishes a direct relationship between the atomic environments and theelectronic bands of semiconductor heterostructures. The framework combines aforward and a reverse model: The forward model predicts how the atomicenvironments tune electronic bands; The reverse learning model extractsinformation about the atomic environments that is associated with an input bandstructure image, such as the ones obtained with angle-resolved photoemissionspectroscopy. We demonstrate the framework using silicon/germanium-basedsuperlattices and heterostructures. Our framework offers a physics-informedapproach to designing heterostructures for new phenomena and devicepossibilities for diverse technologies, going beyond trial-and-errorapproaches.

Image to Properties: Extracting Atomic Structure Information from Band Dispersion Images of Semiconductor Heterostructures Using Machine Learning