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 strains remains a challenge, significantly impeding the development of Xene-based energy-electronic devices. Here, we present an effective first-principles machine-learning approach for developing models based on calculated datasets to rapidly and accurately predict the structural and electronic properties of Xenes under different strains. Twelve descriptors with well-defined physical meanings are established. Four different machine-learning algorithms are selected to build models to identify crucial band-gaps. The Decision Tree Regression (DTR) algorithm performs best on both the training and test sets. Tellurene is chosen as the subject of investigation to validate the accuracy of the DTR model. Compared to the DFT computed result (1.230 eV), the DTR model predicted a band-gap of 1.234 eV for Tellurene, demonstrating the model's high reliability and accuracy. Additionally, the valence of the element in Xenes is the most important feature in the DTR model. Our work demonstrates a promising first-principles machine-learning approach, representing a crucial stride towards accelerating the prediction of key-component properties under different strains for Xene-based energy-electronic devices. [Display omitted] •Accelerating prediction of Xenes’ properties under different strains in 2D devices.•Unveiling Xenes' properties by a new first-principles machine-learning approach.•Demonstrating the superior performance of DTR in identifying band-gap of Xenes.•Validating DTR's high reliability via accurate prediction of Tellurene's band-gap.•Advancing Xene-based energy-electronic device development at harsh conditions.