Several computational methods have been applied to the research to develop new materials. For example, according to the considered scale, they are divided into finite element methods, Monte Carlo methods, molecular dynamics methods, etc. In addition, the experimental data analysis methods include experimental design, neural network, genetic algorithm, etc., which require high-performance computing devices, long computational time and a considerable amount of experimental analysis.
In contrast, grey models are widely used in data analysis as they can improve prediction accuracy from a small amount of poor data. Grey models are mainly applied in various fields such as energy consumption and prediction and CO2 gas emission, attracting a great deal of interest of many researchers. This has led to active research into grey models but few of the results have been applied to the study of material properties.
Pang Chol Ho, a researcher at the Faculty of Material Science and Technology, proposed a hybrid exponential smoothing method, developed a grey model combined with a structural adaptive discrete grey Bernoulli model, and predicted some properties of material.
The comparison analysis with other grey models showed that the proposed model has the highest predictive accuracy.
He used the proposed model to predict various properties of material. The results showed that the predictive accuracy (MAPE) was 0.007 65 for tensile strength, 0.016 52 for Branell hardness and 0.025 15 for the thermoelectric performance parameters of high-entropy alloy AgSnSbSe1.5. This means that the proposed model is effective for predicting material properties.
You can find more information in his paper “Application of Hybrid Grey Bernoulli Model in Material Research” in “Proceedings of KUTIC-2025”.