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

Recent advances in the numerical representation of materials opened the wayfor successful machine learning of grain boundary (GB) energies and theclassification of GB mobility and shear coupling. Two representations wereneeded for these machine learning applications: 1) the ASR representation,based on averaged local environment descriptors; and 2) the LER descriptor,based on fractions of globally unique local environments within the entire GBsystem. We present a detailed tutorial on how to construct these tworepresentations to learn energy, mobility and shear coupling. Additionally, wecatalog some of the null results encountered along the way.

Structural Characterization of Grain Boundaries and Machine Learning of Grain Boundary Energy and Mobility