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Various factors affect the interfacial thermal resistance (ITR) between two materials, making ITR prediction a high-dimensional mathematical problem. Machine learning is a cost-effective method to address this. Here, we report ITR predictive models based on experimental data. The physical, chemical, and material properties of ITR are categorized into three sets of descriptors, and three algorithms are used for the models. Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%. Over 80,000 material systems composed of 293 materials were inputs for predictions. Among the top-100 high-ITR predictions by the three different algorithms, 25 material systems are repeatedly predicted by at least two algorithms. One of the 25 material systems, Bi/Si achieved the ultra-low thermal conductivity in our previous work. We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications. This study proposed a strategy for material exploration for thermal management by means of machine learning.

Predicting the interfacial thermal resistance (ITR) for various material systems is a time-consuming process. In this study, we applied our previously proposed ITR machine learning models to discover the material systems that satisfy both high transparency and low thermal conductivity. The selected material system of TiO2/SiO2 shows a high ITR of 26.56 m2K/GW, which is in good agreement with the predicted value. The nanoscale layered TiO2/SiO2 thin films synthesized by sputtering exhibits ultralow thermal conductivity (0.21 W/mK) and high transparency (>90%, 380–800 nm). The reduction of the thermal conductivity is achieved by the high density of the interfaces with a high ITR rather than the change of the intrinsic thermal conductivity. The thermal conductivity of TiO2 is observed to be 1.56 W/mK with the film thickness in the range of 5–50 nm. Furthermore, the strong substrate dependence is confirmed as the thermal conductivity of the nanoscale layered TiO2/SiO2 thin films on quartz glass is three times lower than that on Si. The proposed TiO2/SiO2 composites have higher transparency and robustness, good adaptivity to electronics, and lower cost than the current transparent thermal insulating materials such as aerogels and polypropylene. The good agreement of the experimental ITR with the prediction and the low thermal conductivity of the layered thin films promise this strategy has great potential for accelerating the development of transparent thermal insulators.

Data-driven material research for property prediction and material design using machine learning methods requires a large quantity, wide variety, and high-quality materials data. For battery materials, which are commonly polycrystalline, ceramics, and composites, multiscale data on substances, materials, and batteries are required. In this work, we develop a data network composed of three interlinked databases, from which we can obtain comprehensive data on substances such as crystal structures and electronic structures, data on materials such as chemical composition, structure, and properties, and data on batteries such as battery composition, operation conditions, and capacity. The data are extracted from research papers on solid electrolytes and cathode materials, selected by screening more than 330 thousand papers using natural language processing tools. Data extraction and curation are carried out by editors specialized in material science and trained in data standardization.

Wide-bandgap gallium nitride (GaN)-based semiconductors offer significant advantages over traditional Si-based semiconductors in terms of high-power and high-frequency operations. As it has superior properties, such as high operating temperatures, high-frequency operation, high breakdown electric field, and enhanced radiation resistance, GaN is applied in various fields, such as power electronic devices, renewable energy systems, light-emitting diodes, and radio frequency (RF) electronic devices. For example, GaN-based high-electron-mobility transistors (HEMTs) are used widely in various applications, such as 5G cellular networks, satellite communication, and radar systems. When a current flows through the transistor channels during operation, the self-heating effect (SHE) deriving from joule heat generation causes a significant increase in the temperature. Increases in the channel temperature reduce the carrier mobility and cause a shift in the threshold voltage, resulting in significant performance degradation. Moreover, temperature increases cause substantial lifetime reductions. Accordingly, GaN-based HEMTs are operated at a low power, although they have demonstrated high RF output power potential. The SHE is expected to be even more important in future advanced technology designs, such as gate-all-around field-effect transistor (GAAFET) and three-dimensional (3D) IC architectures. Materials with high thermal conductivities, such as silicon carbide (SiC) and diamond, are good candidates as substrates for heat dissipation in GaN-based semiconductors. However, the thermal boundary resistance (TBR) of the GaN/substrate interface is a bottleneck for heat dissipation. This bottleneck should be reduced optimally to enable full employment of the high thermal conductivity of the substrates. Here, we comprehensively review the experimental and simulation studies that report TBRs in GaN-on-SiC and GaN-on-diamond devices. The effects of the growth methods, growth conditions, integration methods, and interlayer structures on the TBR are summarized. This study provides guidelines for decreasing the TBR for thermal management in the design and implementation of GaN-based semiconductor devices.

Heat transfer at interfaces plays a critical role in material design and device performance. Higher interfacial thermal resistances (ITRs) affect the device efficiency and increase the energy consumption. Conversely, higher ITRs can enhance the figure of merit of thermoelectric materials by achieving ultra-low thermal conductivity via nanostructuring. This study proposes a dataset of descriptors for predicting the ITRs. The dataset includes two parts: one part consists of ITRs data collected from 87 experimental papers and the other part consists of the descriptors of 289 materials, which can construct over 80,000 pair-material systems for ITRs prediction. The former part is composed of over 1300 data points of metal/nonmetal, nonmetal/nonmetal, and metal/metal interfaces. The latter part consists of physical and chemical properties that are highly correlated to the ITRs. The synthesis method of the materials and the thermal measurement technique are also recorded in the dataset for further analyses. These datasets can be applied not only to ITRs predictions but also to thermal-property predictions or heat transfer on various material systems.Measurement(s)material entity • interface • Material DescriptionTechnology Type(s)machine learning • digital curationMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11618253

Carbon-coated Li4Ti5O12 (LTO) has been prepared using polyimide (PI) as a carbon source via the thermal imidization of polyamic acid (PAA) followed by a carbonization process. In this study, the PI with different structures based on pyromellitic dianhydride (PMDA), 4,4′-oxydianiline (ODA), and p-phenylenediamine (p-PDA) moieties have been synthesized. The effect of the PI structure on the electrochemical performance of the carbon-coated LTO has been investigated. The results indicate that the molecular arrangement of PI can be improved when the rigid p-PDA units are introduced into the PI backbone. The carbons derived from the p-PDA-based PI show a more regular graphite structure with fewer defects and higher conductivity. As a result, the carbon-coated LTO exhibits a better rate performance with a discharge capacity of 137.5 mAh/g at 20 C, which is almost 1.5 times larger than that of bare LTO (94.4 mAh/g).

We present a computational approach for identifying the important descriptors of the ionic conductivities of lithium solid electrolytes. Our approach discriminates the factors of both bulk and grain boundary conductivities, which have been rarely reported. The effects of the interrelated structural (e.g. grain size, phase), material (e.g. Li ratio), chemical (e.g. electronegativity, polarizability) and experimental (e.g. sintering temperature, synthesis method) properties on the bulk and grain boundary conductivities are investigated via machine learning. The data are trained using the bulk and grain boundary conductivities of Li solid conductors at room temperature. The important descriptors are elucidated by their feature importance and predictive performances, as determined by a nonlinear XGBoost algorithm: (i) the experimental descriptors of sintering conditions are significant for both bulk and grain boundary, (ii) the material descriptors of Li site occupancy and Li ratio are the prior descriptors for bulk, (iii) the density and unit cell volume are the prior structural descriptors while the polarizability and electronegativity are the prior chemical descriptors for grain boundary, (iv) the grain size provides physical insights such as the thermodynamic condition and should be considered for determining grain boundary conductance in solid polycrystalline ionic conductors.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Efficient heat dissipation is crucial for electronics. Interfacial thermal resistance (ITR) poses considerable challenges that require innovative solutions. Machine learning approaches could enhance ITR predictions by analysing large datasets to guide the development of inorganic, amorphous and 2D materials for advanced thermal management in next-generation electronic devices.

Trichomonas vaginalis is a prevalent causative agent that causes trichomoniasis leading to uropathogenic inflammation in the host. The crucial role of the actin cytoskeleton in T . vaginalis cytoadherence has been established but the associated signaling has not been fully elucidated. The present study revealed that the T . vaginalis second messenger PIP 2 is located in the recurrent flagellum of the less adherent isolate and is more abundant around the cell membrane of the adherent isolates. The T . vaginalis phosphatidylinositol-4-phosphate 5-kinase ( Tv PI4P5K) with conserved activity phosphorylating PI(4)P to PI(4, 5)P 2 was highly expressed in the adherent isolate and partially colocalized with PIP 2 on the plasma membrane but with discrete punctate signals in the cytoplasm. Plasma membrane PIP 2 degradation by phospholipase C (PLC)-dependent pathway concomitant with increasing intracellular calcium during flagellate-amoeboid morphogenesis. This could be inhibited by Edelfosine or BAPTA simultaneously repressing parasite actin assembly, morphogenesis, and cytoadherence with inhibitory effects similar to the iron-depleted parasite, supporting the significance of PIP 2 and iron in T . vaginalis colonization. Intriguingly, iron is required for the optimal expression and cell membrane trafficking of Tv PI4P5K for in situ PIP 2 production, which was diminished in the iron-depleted parasites. Tv PI4P5K-mediated PIP 2 signaling may coordinate with iron to modulate T . vaginalis contact-dependent cytolysis to influence host cell viability. These observations provide novel insights into T . vaginalis cytopathogenesis during the host-parasite interaction.