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/

Zendy Plus

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

Zendy Tools

Zendy Open

In this paper, we propose a selective sampling procedure to preferentiallyevaluate a potential energy surface (PES) in a part of the configuration spacegoverning a physical property of interest. The proposed sampling procedure isbased on a machine learning method called the Gaussian process (GP), which isused to construct a statistical model of the PES for identifying the region ofinterest in the configuration space. We demonstrate the efficacy of theproposed procedure for atomic diffusion and ionic conduction, specifically theproton conduction in a well-studied proton-conducting oxide, barium zirconateBaZrO3. The results of the demonstration study indicate that our procedure canefficiently identify the low-energy region characterizing the proton conductionin the host crystal lattice, and that the descriptors used for the statisticalPES model have a great influence on the performance.

physical review. b./physical review. b

A machine learning-based selective sampling procedure for identifying  the low energy region in a potential energy surface: a case study on proton  conduction in oxides