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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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Playback language: English
Abstract
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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