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

Oxide perovskite materials of type ABO3 have a wide range of technologicalapplications, such as catalysts in solid oxide fuel cells and aslight-absorbing materials in solar photovoltaics. These materials often exhibitdifferential structural and electrostatic properties through lanthanide ornon-lanthanide derived A- and B- sites. Although, experimental and/orcomputational verification of these differences are often difficult. In thispaper, we thus take a data-driven approach. Specifically, we run three analysisusing the dataset Li, Jacobs, and Morgan [2018a] applying advanced machinelearning tools to perform nonparametric regressions and also to produce datavisualizations using latent factor analysis (LFA) and principal componentanalysis (PCA). We also implement a nonparametric feature screening step whileperforming our high dimensional regression analysis, ensuring robustness in ourresults

Linking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods