We combine machine learning and theoretical modelling to predict and analyze the thermal and molecular dynamics properties of metallic glasses. First, we use machine learning models to estimate the glass transition temperature (Tg) from alloy compositions. Our approach uses the most minimal input features to date, simp...
Ngo T. Que, Hanh Bich Vu, Nguyen T. T. Duyen et al.· Journal of Physics, Conferen...· 0 citations
Designing broadband metamaterial absorbers with high absorption over a wide spectral range remains a significant challenge. Here, we develop an integrated simulation and machine-learning framework for the rapid modeling and design of broadband metamaterial absorbers. Electromagnetic simulations are first performed an...
Nguyen T. T. Duyen, Ngo T. Que, Quynh Le-Van et al.· ACS Applied Optical Material...· 0 citations
This study explores data-driven approaches for predicting the thermal decomposition temperature of polymers using both classical machine learning (CML) models and a small language model (SLM), suggesting that small language models can serve as a valuable alternative modeling strategy for predicting polymer thermal prop...
Nguyen T. T. Duyen, Ngo T. Que, Hanh Bich Vu· Journal of Physics, Conferen...· 0 citations
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