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High-throughput screening and material informatics have shown a great power in the discovery of novel materials, including batteries, high entropy alloys, and photocatalysts. However, the lattice thermal conductivity ( κ ) oriented high-throughput screening of advanced thermal materials is still limited to the intensive use of first principles calculations, which is inapplicable to fast, robust, and large-scale material screening due to the unbearable computational cost demanding. In this study, 15 machine learning algorithms are utilized for fast and accurate κ prediction from basic physical and chemical properties of materials. The well-trained models successfully capture the inherent correlation between these fundamental material properties and κ for different types of materials. Moreover, deep learning combined with a semi-supervised technique shows the capability of accurately predicting diverse κ values spanning 4 orders of magnitude, especially the power of extrapolative prediction on 3716 new materials. The developed models provide a powerful tool for large-scale advanced thermal functional materials screening with targeted thermal transport properties. 

The success of graphene created a new era in materials science, especially for two-dimensional (2D) materials. 2D single-crystal carbon nitride (C 3 N) is the first and only crystalline, hole-free, single-layer carbon nitride and its controlled large-scale synthesis has recently attracted tremendous interest in thermal transport. Here, we performed a comparative study of thermal transport between monolayer C 3 N and the parent graphene, and focused on the effect of temperature and strain on the thermal conductivity ( κ ) of C 3 N, by solving the phonon Boltzmann transport equation (BTE) based on first-principles calculations. The κ of C 3 N shows an anomalous temperature dependence, and the κ of C 3 N at high temperatures is larger than the expected value following the common trend of κ ∼ 1/ T . Moreover, the κ of C 3 N is found to be increased by applying a bilateral tensile strain, despite its similar planar honeycomb structure to graphene. The underlying mechanism is revealed by providing direct evidence for the interaction between lone-pair N-s electrons and bonding electrons from C atoms in C 3 N based on the analysis of orbital-projected electronic structures and electron localization function (ELF). Our research not only conduct a comprehensive study on the thermal transport in graphene-like C 3 N, but also reveal the physical origin of its anomalous properties, which would have significant implications on the future studies of nanoscale thermal transport. 

A central aim of neuroethological research is to discover the mechanisms of natural behaviors in controlled laboratory studies. This goal, however, comes with challenges, namely the selection of experimental paradigms that allow full expression of natural behaviors. Here, we explore this problem in echolocating bats that evolved Doppler shift compensation (DSC) of sonar vocalizations to yield close matching between echo frequency and hearing sensitivity. We ask if behavioral tasks influence the precision of DSC in Pratt’s roundleaf bat, Hipposideros pratti , in three classic laboratory paradigms evoking audio-vocal adjustments: Stationary bats listening to echo playbacks, bats transported on a moving pendulum, and bats flying freely. We found that experimental conditions had a strong influence on the expression of the audiovocal frequency adjustments in bats. H. pratti exhibited robust DSC in both free-flying and moving-pendulum experiments but did not exhibit consistent audiovocal adjustments in echo playback experiments. H. pratti featured a maximum compensation magnitude of 87% and a compensation precision of 0.27% in the free flight experiment. Interestingly, in the moving pendulum experiment H. pratti displayed surprisingly high-precision DSC, with an 84% maximum compensation magnitude and a 0.27% compensation precision. Such DSC performance places H. pratti among the bat species exhibiting the most precise audio-vocal control of echo frequency. These data support the emerging view that Hipposiderid bats have a high-precision DSC system and highlight the importance of selecting experimental paradigms that yield the expression of robust natural behaviors. 

High-throughput computational screening of materials with targeted thermal conductivity ( κ ) plays an important role in promoting the advancement of material design and enormous applications. The Slack model has been widely applied for the fast evaluation of κ with minimal time and resources, showing the potential capability of high-throughput screening of κ . However, after examining the Slack model on a large set of 353 materials, a huge discrepancy is found between the predicted κ and the correspondingly measured κ in experiments for some materials in addition to the generally overestimated κ by the Slack model. Thus, it is necessary to optimize the Slack model for efficiently and accurately evaluating κ . In this study, based on the high-throughput comparison of the κ predicted by the Slack model using elastic properties and those measured in experiments, an optimized Slack model is proposed. As a result, the κ predicted by the optimized Slack model agrees reasonably with the κ measured in experiments, which is much better than the previous prediction. The optimized Slack model proposed in this study can be used for further high-throughput computational evaluation of κ , which would be helpful for finding materials of ultrahigh or ultralow κ with broad applications. 

High performance thermal management is of great significance to the data security and working stability of magnetic devices with broad applications from sensing to data storage and spintronics, where there would exist coupling between the spin and phonon (lattice vibrations). However, the knowledge of the spin effect on thermal transport is lacking. Here, we report that the thermal conductivity of monolayer CrI 3 is more than two orders of magnitude enhanced by the spin–lattice coupling. Fundamental understanding is achieved by analyzing the coupling among electronic, magnetic and phononic properties based on the orbital projected electronic structure and spin density. The bond angles and atomic positions are substantially changed due to the spin–lattice coupling, making the structure more stiff and more symmetric, and lead to the weaker phonon anharmonicity, and thus the enhanced thermal conductivity. This study uncovers the giant effect of spin–lattice coupling on the thermal transport, which would deepen our understanding on thermal transport and shed light on future research of thermal transport in magnetic materials.