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

Establishing a data-driven pipeline for the discovery of novel materialsrequires the engineering of material features that can be feasibly calculatedand can be applied to predict a material's target properties. Here we propose anew class of descriptors for describing crystal structures, which we termRobust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap andis shown to accurately predict a range of material properties. These simple andintuitive class of descriptors are generated from the energetics of a materialat a low level of theory using an incomplete ab initio calculation. Wedemonstrate how the incorporation of ROSA descriptors in ML-based propertyprediction leads to accurate predictions over a wide range of crystals,amorphized crystals, metal-organic frameworks and molecules. We believe thatthe low computational cost and ease of use of these descriptors willsignificantly improve ML-based predictions.

Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors