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

Intrinsically low lattice thermal conductivity ($\kappa_l$) is a desiredrequirement in many crystalline solids such as thermal barrier coatings andthermoelectrics. Here, we design an advanced machine-learning (ML) model basedon crystal graph convolutional neural network that is insensitive to volumes(i.e., scale) of the input crystal structures to discover novel quaternarychalcogenides, AMM'Q$_3$ (A/M/M'=alkali, alkaline-earth, post-transitionmetals, lanthanides, Q=chalcogens). Upon screening the thermodynamic stabilityof $\sim$ 1 million compounds using the ML model iteratively and performingdensity functional theory (DFT) calculations for a small fraction of compounds,we discover 99 compounds that are validated to be stable in DFT. Taking severalDFT-stable compounds, we calculate their $\kappa_l$ using phonon-Boltzmanntransport equation, which reveals ultralow-$\kappa_l$ ($<$ 2 Wm$^{-1}$K$^{-1}$at room-temperature) due to their soft elasticity and strong phononanharmonicity. Our work demonstrates the high-efficiency of scale-invariant MLmodel in predicting novel compounds and presents experimental researchopportunities with these new compounds.

Scale-invariant Machine-learning Model Accelerates the Discovery of Quaternary Chalcogenides with Ultralow Lattice Thermal Conductivity