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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

CoPt nanoparticle catalysts are integral to commercial fuel cells. Suchsystems are prohibitive to fully characterize with electronic structurecalculations. Machine-learned potentials offer a scalable solution; however,such potentials are only reliable if representative training data can beemployed, which typically requires large electronic structure calculations.Here, we use the nearsighted-force training approach to make high-fidelitymachine-learned predictions on large nanoparticles with $>$5,000 atoms usingonly systematically generated small structures ranging from 38-168 atoms. Theresulting ensemble model shows good accuracy and transferability in describingrelative energetics for CoPt nanoparticles with various shapes, sizes and Cocompositions. It is found that the fcc(100) surface is more likely to form aL1$_0$ ordered structure than the fcc(111) surface. The energy convex hull ofthe icosahedron shows the most stable particles have Pt-rich skins and Co-richunderlayers. Although the truncated octahedron is the most stable shape acrossall sizes of Pt nanoparticles, a crossover to icosahedron exists due to a largedownshift of surface energy for CoPt nanoparticle alloys. The downshift can beattributed to strain release on the icosahedron surface due to Co alloying. Weintroduced a simple empirical model to describe the role of Co alloying in thecrossover for CoPt nanoparticles. With Monte-Carlo simulations we additionallysearched for the most stable atomic arrangement for a truncated octahedron withequal Pt and Co compositions, and also we studied its order-disorder phasetransition. We validated the most stable configurations with a new highlyscalable density functional theory code called SPARC. Lastly, theorder-disorder phase transition for a CoPt nanoparticle exhibits a lowertransition temperature and a smoother transition, compared to the bulk CoPtalloy.

The phase stability of large-size nanoparticle alloy catalysts at ab initio quality using a nearsighted force-training approach