Designing materials with targeted lattice thermal conductivity(LTC)demands electronic-level insight into chemical bonding.We introduce two bonding descriptors,namely normalized negative integrated COHP(-ICOHP)and norm...Designing materials with targeted lattice thermal conductivity(LTC)demands electronic-level insight into chemical bonding.We introduce two bonding descriptors,namely normalized negative integrated COHP(-ICOHP)and normalized integrated COBI,that correlate strongly with LTC and rattling(meansquared displacement),surpassing empirical rules and the unnormalized−ICOHP across>4500 inorganic crystals by first-principles.We train a crystal attention graph neural network(CATGNN)to predict these descriptors and screen~200,000 database structures for extreme LTCs.From 367(533)candidates with low(high)normalized-ICOHP and normalized ICOBI,first-principles validation identifies 106 dynamically stable compounds with LTC<5Wm^(−1)K^(−1)(68%<2Wm^(−1)K^(−1))and 13 stable compounds with LTC>100Wm^(−1)K^(−1).The descriptors’low cost and clear physical meaning provide a rapid,reliable route to high-throughput discovery and inverse design of crystalline materials with ultralow or ultrahigh LTC for applications in thermal insulation,thermoelectrics,and electronics cooling.展开更多
基金supported in part by the NSF(award numbers 2110033,2311202,2320292)SC EPSCoR/IDeA Program under NSF OIA-1655740(23-GC01)+2 种基金R.R.acknowledges financial support by the Severo Ochoa Centers of Excellence Program under grant CEX2023-001263-Sby the Generalitat de Catalunya under grant 2021 SGR 01519Calculations were performed at the Centro de Supercomputación de Galicia(CESGA)within actions FI-2023-1-0003,FI-2023-2-0005,and FI-2024-1-0012 of the Red Española de Supercomputación(RES).
文摘Designing materials with targeted lattice thermal conductivity(LTC)demands electronic-level insight into chemical bonding.We introduce two bonding descriptors,namely normalized negative integrated COHP(-ICOHP)and normalized integrated COBI,that correlate strongly with LTC and rattling(meansquared displacement),surpassing empirical rules and the unnormalized−ICOHP across>4500 inorganic crystals by first-principles.We train a crystal attention graph neural network(CATGNN)to predict these descriptors and screen~200,000 database structures for extreme LTCs.From 367(533)candidates with low(high)normalized-ICOHP and normalized ICOBI,first-principles validation identifies 106 dynamically stable compounds with LTC<5Wm^(−1)K^(−1)(68%<2Wm^(−1)K^(−1))and 13 stable compounds with LTC>100Wm^(−1)K^(−1).The descriptors’low cost and clear physical meaning provide a rapid,reliable route to high-throughput discovery and inverse design of crystalline materials with ultralow or ultrahigh LTC for applications in thermal insulation,thermoelectrics,and electronics cooling.
