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

Material informatics is engaged with the application of informatics tools, frequently in the form of machine learning algorithms, to gain insight into structure properties relationships of materials and to design new materials with desired properties. Here we describe the application of such algorithms to the analysis of solar cell (i.e., photovoltaic; PV) libraries made entirely from metal oxides (MOs). MOs-based solar cells hold the potential to provide clean and affordable energy if their power conversion efficiencies are improved. We demonstrate the power of dimensionality reduction methods to visualize the MOs-based solar cell space and the power of several algorithms to develop predictive models for key PV properties. We stress the importance of conducting such studies in collaboration with experimentalists.

Application of Materials Informatics Tools to the Analysis of Combinatorial Libraries of All Metal-Oxides Photovoltaic Cells