Author: H.Kim†, J.Kim†, K.Lee, H.Son, J.Choi, I.Bae, H.Lee, S.Kyung, J.Kim*Title: DFT-verified database and machine learning framework for designing high entropy carbide with superior mechanical propertiesJournal: materials today communicationsYear: 2026Impact factor: 4.5Abstract:High-entropy carbides (HECs) represent a new class of materials that combine multiple principal elements into a single-phase structure, exhibiting exceptional mechanical performance such as high hardness, thermal stability, and wear resistance. However, the extensive compositional space of HECs poses a significant challenge for conventional experimental and computational discovery. In this study, we develop an enhanced crystal graph convolutional neural network (CGCNN) model capable of predicting key elastic properties, specifically bulk and Young’s moduli, directly from crystal structures. By incorporating computational data for solid solutions into the training dataset, the model achieves superior accuracy and generalizability across diverse HEC compositions. Our DFT-verified dataset ensured high reliability and significantly improved prediction performance (R2 = 0.98 vs. 0.92). This highlights the importance of data quality in achieving robust and accurate ML models for materials design. The proposed model successfully identifies five high-performance HEC compositions. These findings demonstrate the capability of machine learning–driven approaches to accelerate HEC discovery and design, offering a cost-effective and efficient pathway to optimize mechanical properties for advanced applications.

Author: H.Kim†, J.Choi†, H.Son, K.Lee, H.Lee, K.Roh, J.Kim*Title: Exploring the potential of out-of-plane MXenes as cathodes for aqueous Al-ion batteries: Ab initio investigationJournal: Journal of Energy StorageYear: 2026Impact factor: 9.8Abstract:Aqueous Al-ion batteries (AAIBs) are promising candidates for next-generation energy storage systems due to their high safety and low cost. However, limitations include their instability and low electrical conductivity. MXenes are gaining attention as cathode materials that can potentially address these challenges owing to their excellent conductivity, cycling stability, and high capacity. We investigated out-of-plane MXenes (where M′ and M′′ = Sc, Ti, V, Cr, and Mn) using ab initio and ab initio molecular dynamics simulations, assessing their structural and aqueous stability, electronic conductivity, Al adsorption behavior, theoretical capacity and voltage profile. Out-of-plane MXenes, particularly Cr2ScC2O2 and Cr2MnC2O2, exhibited suitable aqueous stability and Al adsorption energies. These MXenes achieved Al storage capacities of 392.256 and 374.040 mAh/g, respectively, surpassing graphite (165 mAh/g) and 2D V2O5 (255 mAh/g). Remarkably, Cr2ScC2O2 showed potential to reach a capacity of 588.384 mAh/g. Both MXenes were stable when interacting with AlCl3 electrolyte, suggesting that they are promising for practical use.

Author: J.Park†, J.Kim, H.Chung, J.Choe, H.Kim, S.Lee, S.Kim, H.Jin, J.Kim*, H.Lee*, B.Kim*, J.Kim*Title: Logic-device-inspired mechanical computing system based on three-dimensional active components Journal: npj flexible electronicsYear: 2025Impact factor: 15.5Abstract:Mechanical computing, utilizing mechanical deformation to perform calculations, has attracted significant attention as an innovative computing strategy for achieving high accuracy and exceptional physical robustness. However, its reliance on passive mechanical displacement limits its applicability for complex computations. This study presents a novel system that enables active light signal modulation through reversible mechanical deformation by integrating soft and 3D electronics. The proposed system features: 1) Optical fibers with optimized 3D cracks embedded in a low-modulus, high-elongation material, enabling strain-induced multimodal transitions. 2) Maximized stress concentration on the cracked fibers under strain, allowing them to function as active components for light modulation, which facilitates complex logic calculations and validates truth tables. 3) Multifunctional vibration sensing capabilities, illustrating the scalability of strain inputs and the potential for dynamic applications, such as soft robotics. These findings underscore the potential of this approach as a computational platform for mechanical motion-based technologies.

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