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

Symbolic regression identifies key physical parameters describing materialsproperties by uncovering correlations as nonlinear analytical expressions.However, the pool of expressions grows rapidly with complexity, compromisingits efficiency. We tackle this challenge by a hierarchical approach: identifiedexpressions are used as input parameters for obtaining more complexexpressions. Crucially, this framework can transfer knowledge among properties,highlighting physical relationships. We demonstrate this strategy by using theSure-Independence-Screening-and-Sparsifying-Operator (SISSO) approach toidentify expressions correlated with the lattice constant and cohesive energy,which are then used to model the bulk modulus of ABO3 perovskites.

Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites