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

Recent machine learning methods make it possible to model potential energy ofatomic configurations with chemical-level accuracy (as calculated fromab-initio calculations) and at speeds suitable for molecular dynam- icssimulation. Best performance is achieved when the known physical constraintsare encoded in the machine learning models. For example, the atomic energy isinvariant under global translations and rotations, it is also invariant topermutations of same-species atoms. Although simple to state, these symmetriesare complicated to encode into machine learning algorithms. In this paper, wepresent a machine learning approach based on graph theory that naturallyincorporates translation, rotation, and permutation symmetries. Specifically,we use a random walk graph kernel to measure the similarity of two adjacencymatrices, each of which represents a local atomic environment. This GraphApproximated Energy (GRAPE) approach is flexible and admits many possibleextensions. We benchmark a simple version of GRAPE by predicting atomizationenergies on a standard dataset of organic molecules.

Learning molecular energies using localized graph kernels