Ehsan Alibagheri1, Bohayra Mortazavi2, Timon Rabczuk3,4,*
CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1287-1300, 2021, DOI:10.32604/cmc.2021.013564
Abstract Machine-learning (ML) models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets. For advanced technologies, predicting novel materials and identifying their specification are critical issues. Two-dimensional (2D) materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies. In this work, our objective is to search for desirable properties, such as the electronic band gap and total energy, among others, for which the accelerated prediction is highly appealing, prior to conducting accurate theoretical and experimental investigations. Among all available componential methods, gradient-boosted (GB) ML algorithms are known… More >
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