Bandgap prediction of non-metallic crystals through machine learning approach
Sadhana Barman,
Harkishan Dua,
Utpal Sarkar
Abstract:The determination of bandgap is the heart of electronic structure of any material and is a crucial factor for thermoelectric performance of it. Due to large amount to data (features) that are related to bandgap are now a days available, it is possible to make use of machine learning approach to predict the bandgap of the material. The study commences by selecting the feature through Pearson correlation study between bandgap and various thermoelectric parameters in non-metallic crystals. Among the forty two pa… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.