Rapid and accurate identification of structural and electronic properties of Xenes under different strains via a first-principles machine-learning approach for energy-electronic devices

• Published in: Materials today communications Vol. 40; p. 109904
• Main Authors: Wang, Guoqing, Liu, Rongchao, Gebreslassie, Gebrehiwot, Desta, Halefom G., Tian, Dong, Lin, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Decision Tree Regression; Electronic properties; Energy-electronic devices; First-principles machine-learning approach; Performance prediction; Xenes; Xenes; Performance prediction; Electronic properties; First-principles machine-learning approach; Decision Tree Regression; Energy-electronic devices
• ISSN: 2352-4928; 2352-4928
• DOI: 10.1016/j.mtcomm.2024.109904

Mono-elemental two-dimensional materials (Xenes) are of supreme importance to new-type energy-electronic devices due to their outstanding and unique physical and electronic properties. However, the rapid and accurate identification of the structural and electronic properties of Xenes under various s...