Dong Li^1,*, Yonggang Zheng¹, Hongwu Zhang¹, Hongfei Ye¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09714

Abstract The family of 2D transition-metal oxides and dichalcogenides with 1H phase (1H-MX₂) has sparked great interest from the perspective of basic physics and applied science. Interestingly, their performances could be further regulated and improved through strain engineering. Effective regulation is founded on a wellunderstood mechanical performance, however, the large number of 1H-MX₂ materials has not yet been revealed. Here, a general theoretical model is constructed based on the molecular mechanics, which provides an effective and rapid analytical algorithm for evaluating the mechanical properties of the entire family of 1H-MX₂. The validity of the constructed model is verified by molecular dynamics… More >

Ehsan Alibagheri¹, Bohayra Mortazavi², Timon Rabczuk^3,4,*

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 >