Author: I.Kim†, H.Kim, J.Kim, M.So, J.Choi, M.Kim, J.Kim*Title: Prediction and Virtual Screening of Elastic Properties in High-Entropy Carbides Using Composition-Based Descriptors and Machine LearningJournal: Ceramics InternationalYear: 2026Impact factor: 6.0Abstract:High-entropy carbides (HECs) have attracted considerable attention for extreme-environment applications owing to their compositional flexibility and high-temperature mechanical properties. Among their key properties, Young’s modulus is closely related to stiffness and thermo-mechanical reliability, yet the vast compositional space of HECs makes exhaustive experimental characterization and first-principles screening impractical. Here, we present a composition-based machine-learning framework for predicting Young’s modulus and identifying its key descriptors. Using a high-fidelity density functional theory (DFT)–derived dataset of 146 HEC compositions, random forest (RF), support vector regression (SVR), and artificial neural network (ANN) models were trained. The ANN showed the best test-set performance (R2 = 0.88), while the SVR achieved the best 5-fold cross-validation performance (R2 = 0.87). Feature-importance and SHapley Additive exPlanations (SHAP) analyses revealed that valence electron concentration (VEC)-related descriptors are the most influential factors, with melting temperature- and atomic radius-related descriptors providing complementary contributions. Virtual screening of 6426 quinary compositions identified high-modulus candidates, with (Nb0.25Ta0.25W0.20Ti0.15Hf0.15)C ranked highest (487.44 GPa). These results demonstrate an interpretable and efficient route for data-driven discovery of high-stiffness HECs.

Author: Nilüfer Çakmakçı Lee†, J.Lee, J.Choi, I.Bae, J.Bang, H.Kim, D.Lee, J.Han, J.Kwak, J.Kim*, Y.Jeong*Title: Equal mass, unequal impact: Particle density and bending rigidity of carbon nanotube additives govern silicon anode performanceJournal: Journal of Power SourcesYear: 2026Impact factor: 8.4Abstract:Silicon offers a much higher lithium storage capacity than graphite but suffers from severe volume expansion during cycling, which undermines electrode stability. Carbon nanotubes (CNTs) are widely employed to address this limitation, yet their coupled mechanical and electrical functions remain poorly understood. Here, we systematically compare single-walled (SWCNT) and multi-walled (MWCNT) carbon nanotubes as conductive additives in silicon nanoparticle anodes at identical loadings (0.5–5 wt%). SWCNTs are theoretically estimated to provide over 103 times more individual tubes per gram, forming dense percolation networks that enable rapid charge–discharge performance (2330 mAh g−1 at 3 C, 5 wt%). MWCNTs, though fewer in number, exhibit higher bending rigidity, restricting electrode thickening to ∼50% after 10 cycles. At CNT contents below 1 wt%, capacity retention scales with particle population, emphasizing the dominance of physical contact over intrinsic conductivity. These results decouple the roles of particle density (SWCNT) and mechanical stiffness (MWCNT), offering practical guidelines for optimizing CNT selection in cost-effective, high-energy silicon anodes.

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