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

We present a novel scheme to accurately predict atomic forces as vectorquantities, rather than sets of scalar components, by Gaussian Process (GP)Regression. This is based on matrix-valued kernel functions, on which we imposethe requirements that the predicted force rotates with the target configurationand is independent of any rotations applied to the configuration databaseentries. We show that such covariant GP kernels can be obtained by integrationover the elements of the rotation group SO(d) for the relevant dimensionalityd. Remarkably, in specific cases the integration can be carried outanalytically and yields a conservative force field that can be recast into apair interaction form. Finally, we show that restricting the integration to asummation over the elements of a finite point group relevant to the targetsystem is sufficient to recover an accurate GP. The accuracy of our kernels inpredicting quantum-mechanical forces in real materials is investigated by testson pure and defective Ni, Fe and Si crystalline systems.

Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels