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/

Zendy Plus

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

Zendy Tools

Zendy Open

Recently, machine learning potentials have been advanced as candidates tocombine the high-accuracy of quantum mechanical simulations with the speed ofclassical interatomic potentials. A crucial component of a machine learningpotential is the description of local atomic environments by some set ofdescriptors. These should ideally be continuous throughout the specified localatomic environment, twice-differentiable with respect to atomic positions andcomplete in the sense of containing all possible information about theneighborhood. An updated version of the recently proposed Spherical Besseldescriptors satisfies all three of these properties, and moreover is optimallycomplete in the sense of encoding all configurational information with thesmallest possible number of descriptors. The Smooth Overlap of Atomic Positiondescriptors that are frequently visited in the literature and the Zernikedescriptors that are built upon a similar basis are included into thediscussion as being the natural counterparts of the Spherical Besseldescriptors, and shown to be incapable of satisfying the full list of corerequirements for an accurate description. Aside being mathematically andphysically superior, the Spherical Bessel descriptors have also the advantageof allowing machine learning potentials of comparable accuracy that requireroughly an order of magnitude less computation time per evaluation than theSmooth Overlap of Atomic Position descriptors, which appear to be the commonchoice of descriptors in recent studies.

Continuous and Optimally Complete Description of Chemical Environments Using Spherical Bessel Descriptors