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

Computational methodologies have been critical to our understanding of defects at nanometer scales. These methodologies have been dominated by two classes: quantum mechanics (QM)-based methods and semiempirical/classical methods. The former, while accurate and versatile, are time consuming, while the latter are efficient but limited in versatility and transferability. Recently, machine learning (ML) methods have shown initial promise in bridging these two limitations due to their accuracy and flexibility. In this work, the true capability of ML methods is explored by simulating defects in platinum over several length/time scales. We compare our results with density functional theory (DFT) for atomic-level defect behavior and with experiments for nanolevel behavior. We also compare our predictions with several classical potentials. This work aims to showcase the length/time scales attainable using ML, as well as the complexity they are capable of capturing, demonstrating that these methodologies may be effectively used, in the future, to bridge experiments and QM methods.

Multiscale Modeling of Defect Phenomena in Platinum Using Machine Learning of Force Fields