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

The efficient and economical exploitation of polymers with high thermalconductivity is essential to solve the issue of heat dissipation in organicdevices. Currently, the experimental preparation of functional thermalconductivity polymers remains a trial and error process due to themulti-degrees of freedom during the synthesis and characterization process. Inthis work, we have proposed a high-throughput screening framework for polymerchains with high thermal conductivity via interpretable machine learning andphysical-feature engineering. The polymer thermal conductivity datasets fortraining were first collected by molecular dynamics simulation. Inspired by thedrug-like small molecule representation and molecular force field, 320 polymermonomer descriptors were calculated and the 20 optimized descriptors withphysical meaning were extracted by hierarchical down-selection. All the machinelearning models achieve a prediction accuracy R2 greater than 0.80, which issuperior to that of represented by traditional graph descriptors. Further, thecross-sectional area and dihedral stiffness descriptors were identified forpositive/negative contribution to thermal conductivity, and 107 promisingpolymer structures with thermal conductivity greater than 20.00 W/mK wereobtained. Mathematical formulas for predicting the polymer thermal conductivitywere also constructed by using symbolic regression. The high thermalconductivity polymer structures are mostly {\pi}-conjugated, whose overlappingp-orbitals enable easily to maintain strong chain stiffness and large groupvelocities. The proposed data-driven framework should facilitate thetheoretical and experimental design of polymers with desirable properties.

Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors