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 show that a deep-learning neural network potential (DP) based on densityfunctional theory (DFT) calculations can well describe Cu-Zr materials, anexample of a binary alloy system that can coexist in several orderedintermetallics and as an amorphous phase. The complex phase diagram for Cu-Zrmakes it a challenging system for traditional atomistic force-fields that failto describe well the different properties and phases. Instead, we show that aDP approach using a large database with ~300k configurations can render resultsgenerally on par with DFT. The training set includes configurations of pristineand bulk elementary metals and intermetallics in the liquid and solid phases inaddition to slab and amorphous configurations. The DP model was validated bycomparing bulk properties such as lattice constants, elastic constants, bulkmoduli, phonon spectra, surface energies to DFT values for identicalstructures. Further, we contrast the DP results with values obtained usingwell-established two embedded atom method potentials. Overall, our DP potentialprovides near DFT accuracy for the different Cu-Zr phases but with a fractionof its computational cost, thus enabling accurate computations of realisticatomistic models especially for the amorphous phase.

journal of chemical physics online/the journal of chemical physics/journal of chemical physics

Optimization and Validation of a Deep Learning CuZr Atomistic Potential:  Robust Applications for Crystalline and Amorphous Phases with near-DFT  Accuracy