Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-materialbasis, primarily due to the exponential scaling of model complexity with the number of a...Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-materialbasis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneckwith the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness andprecision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learningand data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralowlattice thermal conductivity (<1 Wm^(−1) K^(−1)) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, aclass of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550quaternary Heuslers, respectively.展开更多
The demand for active and effective management of heat transfer is increasing in various modern application scenarios. The thermal conductivity of materials plays a key role in thermal management systems, and reversib...The demand for active and effective management of heat transfer is increasing in various modern application scenarios. The thermal conductivity of materials plays a key role in thermal management systems, and reversibly tunable thermal properties are one of the fundamental needs for materials. Thermal smart materials, whose thermal properties can be tuned with an external trigger, have attracted the attention of researchers. In this paper, we provide a brief review of current research advances in thermal smart materials in recent years in terms of fundamental physical mechanisms, thermal switching ratios, and their application value. We focus on typical thermal smart materials such as nanoparticle suspensions, phase change materials, polymers, layered materials tuned by electrochemistry and other materials tuned by a specific external field. After surveying the fundamental mechanisms, we present applications of thermal smart components and devices in temperature control, thermal circuits, phonon computers, thermal metamaterials, and so on. Finally, we discuss the limitations and challenges of thermal smart materials, as well as our predictions for future development.展开更多
基金A.R.acknowledges the financial support by the Department of Energy,Office of Nuclear Energy,Integrated University Program Graduate Fellowship(IUP)under award no.DE-NE-0000095NASA SC Space Grant Consortium REAP Program(521383-RP-SC004)+1 种基金H.Y.and B.C.acknowledge the financial support from the National Natural Science Foundation of China(51825601 and U20A20301)Research reported in this work was supported in part by NSF under awards 1905775,2030128,and 2110033.
文摘Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-materialbasis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneckwith the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness andprecision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learningand data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralowlattice thermal conductivity (<1 Wm^(−1) K^(−1)) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, aclass of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550quaternary Heuslers, respectively.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51825601, and U20A20301)。
文摘The demand for active and effective management of heat transfer is increasing in various modern application scenarios. The thermal conductivity of materials plays a key role in thermal management systems, and reversibly tunable thermal properties are one of the fundamental needs for materials. Thermal smart materials, whose thermal properties can be tuned with an external trigger, have attracted the attention of researchers. In this paper, we provide a brief review of current research advances in thermal smart materials in recent years in terms of fundamental physical mechanisms, thermal switching ratios, and their application value. We focus on typical thermal smart materials such as nanoparticle suspensions, phase change materials, polymers, layered materials tuned by electrochemistry and other materials tuned by a specific external field. After surveying the fundamental mechanisms, we present applications of thermal smart components and devices in temperature control, thermal circuits, phonon computers, thermal metamaterials, and so on. Finally, we discuss the limitations and challenges of thermal smart materials, as well as our predictions for future development.
