Machine-learning-assisted modeling of defect-mediated charge transport in anisotropic 2D semiconductors
DOI:
https://doi.org/10.54355/tbusphys/30070147.4.2.2026.0051Keywords:
anisotropic semiconductors, defect-mediated transport, two-dimensional materials, machine learning, carrier mobility, quantum materialsAbstract
This study investigates defect-mediated charge transport in anisotropic two-dimensional semiconductors using combined transport modeling and machine-learning analysis. The objective of the work was to determine how structural defects and lattice anisotropy influence electrical conductivity and carrier mobility in low-dimensional quantum materials. Monolayer transition-metal dichalcogenides, black phosphorus, and ReS2-based systems with various defect configurations were analyzed. Transport behavior was modeled within a semiclassical framework incorporating anisotropic carrier mobility and defect-assisted scattering mechanisms. Machine-learning models, including random forest regression, gradient boosting regression, and artificial neural networks, were applied to predict conductivity variations using structural and electronic descriptors. The results demonstrated that conductivity decreases nonlinearly with increasing defect concentration, particularly above 1013 cm−2, where localization effects become dominant. Strongly anisotropic materials exhibited enhanced sensitivity to vacancy-induced disorder due to directional carrier confinement. Among the evaluated algorithms, the artificial neural network achieved the highest predictive accuracy with an R2 value of 0.972. Feature-importance analysis revealed that defect concentration, effective mass, and lattice anisotropy ratio are the primary factors governing transport degradation. The study confirms that accurate prediction of transport properties in anisotropic quantum materials requires simultaneous consideration of defect physics and directional electronic transport. The proposed approach may be useful for the design and optimization of next-generation nanoelectronic and flexible semiconductor devices.
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J. Kang, S. Tongay, J. Zhou, J. Li, and J. Wu, “Band offsets and heterostructures of two-dimensional semiconductors,” Appl. Phys. Lett., vol. 102, no. 1, Jan. 2013, doi: 10.1063/1.4774090/126471. DOI: https://doi.org/10.1063/1.4774090
S. Manzeli, D. Ovchinnikov, D. Pasquier, O. V. Yazyev, and A. Kis, “2D transition metal dichalcogenides,” Nat. Rev. Mater. 2017 28, vol. 2, no. 8, pp. 17033-, Jun. 2017, doi: 10.1038/natrevmats.2017.33. DOI: https://doi.org/10.1038/natrevmats.2017.33
Y. Liu, N. O. Weiss, X. Duan, H. C. Cheng, Y. Huang, and X. Duan, “Van der Waals heterostructures and devices,” Nat. Rev. Mater. 2016 19, vol. 1, no. 9, pp. 16042-, Jul. 2016, doi: 10.1038/natrevmats.2016.42. DOI: https://doi.org/10.1038/natrevmats.2016.42
H. Schmidt, F. Giustiniano, and G. Eda, “Electronic transport properties of transition metal dichalcogenide field-effect devices: surface and interface effects,” Chem. Soc. Rev., vol. 44, no. 21, pp. 7715–7736, Oct. 2015, doi: 10.1039/C5CS00275C. DOI: https://doi.org/10.1039/C5CS00275C
K. Cho et al., “Electric Stress-Induced Threshold Voltage Instability of Multilayer MoS2 Field Effect Transistors,” ACS Nano, vol. 7, no. 9, pp. 7751–7758, Sep. 2013, doi: 10.1021/NN402348R. DOI: https://doi.org/10.1021/nn402348r
M. Buscema, D. J. Groenendijk, G. A. Steele, H. S. J. Van Der Zant, and A. Castellanos-Gomez, “Photovoltaic effect in few-layer black phosphorus PN junctions defined by local electrostatic gating,” Nat. Commun. 2014 51, vol. 5, no. 1, pp. 4651-, Aug. 2014, doi: 10.1038/ncomms5651. DOI: https://doi.org/10.1038/ncomms5651
A. Carvalho, M. Wang, X. Zhu, A. S. Rodin, H. Su, and A. H. Castro Neto, “Phosphorene: from theory to applications,” Nat. Rev. Mater. 2016 111, vol. 1, no. 11, pp. 16061-, Aug. 2016, doi: 10.1038/natrevmats.2016.61. DOI: https://doi.org/10.1038/natrevmats.2016.61
S. Das, H. Y. Chen, A. V. Penumatcha, and J. Appenzeller, “High Performance Multilayer MoS2 Transistors with Scandium Contacts,” Nano Lett., vol. 13, no. 1, pp. 100–105, Jan. 2012, doi: 10.1021/NL303583V. DOI: https://doi.org/10.1021/nl303583v
A. Jain et al., “Commentary: The materials project: A materials genome approach to accelerating materials innovation,” APL Mater., vol. 1, no. 1, Jul. 2013, doi: 10.1063/1.4812323/119685. DOI: https://doi.org/10.1063/1.4812323
S. M. Nie, Z. Song, H. Weng, and Z. Fang, “Quantum spin Hall effect in two-dimensional transition-metal dichalcogenide haeckelites,” Phys. Rev. B, vol. 91, no. 23, p. 235434, Jun. 2015, doi: 10.1103/PhysRevB.91.235434. DOI: https://doi.org/10.1103/PhysRevB.91.235434
A. Fasolino, J. H. Los, and M. I. Katsnelson, “Intrinsic ripples in graphene,” Nat. Mater. 2007 611, vol. 6, no. 11, pp. 858–861, Sep. 2007, doi: 10.1038/nmat2011. DOI: https://doi.org/10.1038/nmat2011
C. Chen, W. Ye, Y. Zuo, C. Zheng, and S. P. Ong, “Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals,” Chem. Mater., vol. 31, no. 9, pp. 3564–3572, May 2019, doi: 10.1021/ACS.CHEMMATER.9B01294. DOI: https://doi.org/10.1021/acs.chemmater.9b01294
T. Xie and J. C. Grossman, “Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties,” Phys. Rev. Lett., vol. 120, no. 14, p. 145301, Apr. 2018, doi: 10.1103/PhysRevLett.120.145301. DOI: https://doi.org/10.1103/PhysRevLett.120.145301
A. Dunn, Q. Wang, A. Ganose, D. Dopp, and A. Jain, “Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm,” npj Comput. Mater., vol. 6, no. 1, pp. 138-, Dec. 2020, doi: 10.1038/S41524-020-00406-3;SUBJMETA. DOI: https://doi.org/10.1038/s41524-020-00406-3
J. Yang, L. Tang, Y. Wang, J. Wen, and W. Chen, “Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials,” Nanomater. 2026, Vol. 16, Page 650, vol. 16, no. 11, p. 650, May 2026, doi: 10.3390/NANO16110650. DOI: https://doi.org/10.3390/nano16110650
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