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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Simulating collision cascades and radiation damage poses a long-standingchallenge for existing interatomic potentials, both in terms of accuracy andefficiency. Machine-learning based interatomic potentials have shownsufficiently high accuracy for radiation damage simulations, but most existingones are still not efficient enough to model high-energy collision cascadeswith sufficiently large space and time scales. To this end, we here extend thehighly efficient neuroevolution potential (NEP) framework by combining it withthe Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential,obtaining a NEP-ZBL framework. We train a NEP-ZBL model for tungsten anddemonstrate its accuracy in terms of the elastic properties, melting point, andvarious energetics of defects that are relevant for radiation damage. We thenperform large-scale molecular dynamics simulations with the NEP-ZBL model withup to 8.1 million atoms and 240 ps (using a single 40-GB A100 GPU) to study thedifference of primary radiation damage in both bulk and thin-foil tungsten.While our findings for bulk tungsten are consistent with existing resultssimulated by embedded atom method (EAM) models, the radiation damage differssignificantly in foils and shows that larger and more vacancy clusters as wellas smaller and fewer interstitial clusters are produced due to the presence ofa free surface.

Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten