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

The crystal structure of the MAPbI$_3$ hybrid perovskite forms an intricateelectrostatic puzzle with different ordering patterns of the MA molecules atelevated temperatures. For this perovskite three published model Hamiltoniansbased on the point-dipole (pd) approximation combined with short-rangeeffective interactions are compared to a recently developed machine-learningforce field. A molecular order parameter is used to consistently compare thetransformation of the anti-ferroelectric ordering in the orthorhombic phaseupon raising the temperature. We show that the ground states and theorder-disorder transition of the three models are completely different. Ouranalysis indicates that the long-range order in the low-temperatureorthorhombic phase can be captured by pd-based models with a short cutoffradius, including the nearest and next-nearest neighbor molecules. Byconstructing effective atomic interactions the ordering can already bedescribed within 6 A radius. By extracting the coupling energetics of themolecules from density functional theory calculations onMA$_x$Cs$_{1-x}$PbI$_3$ test systems, we show that the pd-approximation holdsat least for static structures. To improve the accuracy of the pd-interactionan Ewald summation is applied combined with a distance dependent electronicscreening function.

Long-range order imposed by short-range interactions in methylammonium lead iodide: Comparing point-dipole models to machine-learning force fields