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Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets

Engineering and Technology

Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets

P. Huang, R. Lukin, et al.

Discover the insights from the groundbreaking 2D Material Defect (2DMD) datasets, which unveil the defect properties of 2D materials through DFT calculations. This research, conducted by Pengru Huang, Ruslan Lukin, Maxim Faleev, Nikita Kazeev, Abdalaziz Rashid Al-Maeeni, Daria V. Andreeva, Andrey Ustyuzhanin, Alexander Tormasov, A. H. Castro Neto, and Kostya S. Novoselov, seeks to provide a data-driven understanding of defect behaviors to enhance machine learning models for materials design.

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