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/

Global: Zendy Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The Materials Genome Initiative requires the crossing of material calculations, machine learning, and experiments to accelerate the material development process. In recent years, data-based methods have been applied to the thermoelectric field, mostly on the transport properties. In this work, we combined data-driven machine learning and first-principles automated calculations into an active learning loop, in order to predict the p-type power factors (PFs) of diamond-like pnictides and chalcogenides. Our active learning loop contains two procedures (1) based on a high-throughput theoretical database, machine learning methods are employed to select potential candidates and (2) computational verification is applied to these candidates about their transport properties. The verification data will be added into the database to improve the extrapolation abilities of the machine learning models. Different strategies of selecting candidates have been tested, finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy (the Pearson  R  = 0.95 on untrained systems). Based on the prediction from the machine learning models, binary pnictides, vacancy, and small atom-containing chalcogenides are predicted to have large PFs. The bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds.

Active learning for the power factor prediction in diamond-like thermoelectric materials