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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Data-driven approaches to solve problems in materials science have gainedimmense popularity in recent times due to their ability to predict unknownmaterial properties and uncover relationships between structure and property.Machine learning algorithms like GBRT, random forest and neural networks havehad tremendous success in predicting target properties of materials and designof structures for various applications. However, a major drawback for achievingresults within the required accuracy using these algorithms has been the needfor large datasets which can be challenging for problems when data is notsufficiently available for training the models. In this work, we propose theuse of a hierarchical clustering algorithm which can work considerably betteron materials science problems with small dataset constraints. We apply thealgorithm to screen out promising double perovskite materials as candidates forsolar cells.

Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering