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

Reliable artificial-intelligence models have the potential to accelerate thediscovery of materials with optimal properties for various applications,including superconductivity, catalysis, and thermoelectricity. Advancements inthis field are often hindered by the scarcity and quality of available data andthe significant effort required to acquire new data. For such applications,reliable surrogate models that help guide materials space exploration usingeasily accessible materials properties are urgently needed. Here, we present ageneral, data-driven framework that provides quantitative predictions as wellas qualitative rules for steering data creation for all datasets via acombination of symbolic regression and sensitivity analysis. We demonstrate thepower of the framework by generating an accurate analytic model for the latticethermal conductivity using only 75 experimentally measured values. Byextracting the most influential material properties from this model, we arethen able to hierarchically screen 732 materials and find 80 ultra-insulatingmaterials.

Accelerating Materials-Space Exploration for Thermal Insulators by Mapping Materials Properties via Artificial Intelligence