This paper introduces the 2D Material Defect (2DMD) datasets, which include defect properties of 2D materials calculated using DFT. The study provides a data-driven physical understanding of defect behaviors, aiming to guide the development of efficient machine learning models for materials design with predetermined properties.
Publisher
npj 2D Materials and Applications
Published On
Feb 01, 2023
Authors
Pengru Huang, Ruslan Lukin, Maxim Faleev, Nikita Kazeev, Abdalaziz Rashid Al-Maeeni, Daria V. Andreeva, Andrey Ustyuzhanin, Alexander Tormasov, A. H. Castro Neto, Kostya S. Novoselov
Tags
2D materials
defect properties
DFT calculations
machine learning
materials design
data-driven understanding
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