연구실에서 박사과정을 마친 김효경 박사가 한국화학융합시험연구원에 합격했습니다. 축하드립니다~!

연구실에서 석사과정을 마친 손하영 석사가 고려아연에 합격하였습니다. 축하드립니다.

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연구실에서 석사과정을 마친 곽종욱 석사가 나노융합산업협회에 합격하였습니다. 축하드립니다.

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.