Application of machine learning methods to predict the optical absorption coefficient of composite ceramics based on hydroxyapatite
Rezvanova A.E.1, Kudryashov B.S.1, Ponomarev A.N.1,2
1Institute of Strength Physics and Materials Science of Siberian Branch of Russian Academy of Sciences, Tomsk, Russia
2Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russia
Email: ranast@ispms.ru
Models for predicting the optical absorption coefficient of hydroxyapatite-based ceramics and composites with additives of 0.1 and 0.5 wt.% multi-walled carbon nanotubes additives in the terahertz radiation frequency range from 0.2 to 1.4 THz were constructed based on experimental data using machine learning methods. The lowest value of the mean absolute error was shown by modeling using methods of adaptive boosting (0.951 %) and neural networks (0.049 %). The results of numerical simulation confirm that the use of machine learning methods makes it possible to predict the absorption coefficient with high accuracy for ceramic materials with carbon nanotube additives in the range from 0 to 0.5 wt.% concentrations. The obtained results make it possible to optimize the composition of hydroxyapatite-based ceramics to control their optical characteristics. Keywords: prediction, regression analysis, machine learning, neural networks, hydroxyapatite, multi-walled carbon nanotubes, absorption coefficient.
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