This article presents a study that uses high-throughput screening and machine learning to identify promising van der Waals (vdW) dielectric materials for two-dimensional (2D) nanoelectronics, specifically for field-effect transistors (FETs). Researchers screened over 500 materials from the Materials Project database, assessing properties like bandgap, dielectric constant, and structural stability. Nine materials emerged as highly suitable candidates for integration with MoS2-based FETs, displaying desirable dielectric and band alignment properties. The team developed a machine learning classifier to further streamline the screening, ultimately identifying 49 additional vdW materials with potential applications in 2D electronics. This work paves the way for enhanced material selection in developing efficient, scalable 2D semiconductor devices.
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https://www.nature.com/articles/s41467-024-53864-4
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