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

Machine learning (ML) is widely used to explore crystal materials and predicttheir properties. However, the training is time-consuming for deep-learningmodels, and the regression process is a black box that is hard to interpret.Also, the preprocess to transfer a crystal structure into the input of ML,called descriptor, needs to be designed carefully. To efficiently predictimportant properties of materials, we propose an approach based on ensemblelearning consisting of regression trees to predict formation energy and elasticconstants based on small-size datasets of carbon allotropes as an example.Without using any descriptor, the inputs are the properties calculated bymolecular dynamics with 9 different classical interatomic potentials. Overall,the results from ensemble learning are more accurate than those from classicalinteratomic potentials, and ensemble learning can capture the relativelyaccurate properties from the 9 classical potentials as criteria for predictingthe final properties.

Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example