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

Based on an analysis of the short range chemical environment of each atom ina system, standard machine learning based approaches to the construction ofinteratomic potentials aim at determining directly the central quantity whichis the total energy. This prevents for instance an accurate description of theenergetics of systems where long range charge transfer is important as well asof ionized systems. We propose therefore not to target directly with machinelearning methods the total energy but an intermediate physical quantity namelythe charge density, which then in turn allows to determine the total energy. Byallowing the electronic charge to distribute itself in an optimal way over thesystem, we can describe not only neutral but also ionized systems withunprecedented accuracy. We demonstrate the power of our approach for bothneutral and ionized NaCl clusters where charge redistribution plays a decisiverole for the energetics. We are able to obtain chemical accuracy, i.e. errorsof less than a milli Hartree per atom compared to the reference densityfunctional results. The introduction of physically motivated quantities whichare determined by the short range atomic environment via a neural network leadsalso to an increased stability of the machine learning process andtransferability of the potential.

Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network