High-throughput screening and machine learning classification of van der Waals dielectrics for 2D nanoelectronics

Yuhui Li, Guolin Wan, Yongqian Zhu, Jingyu Yang, Yan-Fang Zhang, Jinbo Pan () and Shixuan Du ()
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Yuhui Li: Chinese Academy of Sciences
Guolin Wan: Chinese Academy of Sciences
Yongqian Zhu: Chinese Academy of Sciences
Jingyu Yang: Chinese Academy of Sciences
Yan-Fang Zhang: Chinese Academy of Sciences
Jinbo Pan: Chinese Academy of Sciences
Shixuan Du: Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Van der Waals (vdW) dielectrics are promising for enhancing the performance of nanoscale field-effect transistors (FETs) based on two-dimensional (2D) semiconductors due to their clean interfaces. Ideal vdW dielectrics for 2D FETs require high dielectric constants and proper band alignment with 2D semiconductors. However, high-quality dielectrics remain scarce. Here, we employed a topology-scale algorithm to screen vdW materials consisting of zero-dimensional (0D), one-dimensional (1D), and 2D motifs from Materials Project database. High-throughput first-principles calculations yielded bandgaps and dielectric properties of 189 0D, 81 1D and 252 2D vdW materials. Among which, 9 highly promising dielectric candidates are suitable for MoS2-based FETs. Element prevalence analysis indicates that materials containing strongly electronegative anions and heavy cations are more likely to be promising dielectrics. Moreover, we developed a high-accuracy two-step machine learning (ML) classifier for screening dielectrics. Implementing active learning framework, we successfully identified 49 additional promising vdW dielectrics. This work provides a rich candidate list of vdW dielectrics along with a high-accuracy ML screening model, facilitating future development of 2D FETs.

Date: 2024
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DOI: 10.1038/s41467-024-53864-4

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