2021
DOI: 10.48550/arxiv.2110.01961
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Quantum point defects in 2D materials: The QPOD database

Abstract: Atomically thin two-dimensional (2D) materials are ideal host systems for quantum defects as they offer easier characterisation, manipulation and read-out of defect states as compared to their bulk counterparts. Here we introduce the Quantum Point Defect (QPOD) database with more than 1900 defect systems comprising various charge states of 503 intrinsic point defects (vacancies and antisites) in 82 different 2D semiconductors and insulators. The Atomic Simulation Recipes (ASR) workflow framework was used to pe… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 77 publications
(109 reference statements)
0
1
0
Order By: Relevance
“…In recent years, the continuous growth of available computational power 1 has stimulated scientists to move in the direction of high-throughput simulations. [2][3][4][5][6][7][8][9][10] Along this line, open access databases, such as OQMD, 11,12 NOMAD, 13,14 Aflowlib, 15 C2DB, 16,17 QPOD, 18 Materials Project, 19 Materials Cloud, 20 and related AiiDA, 21,22 provide researchers with a huge collection of basic first-principles results. A large amount of ab initio data is thus available, which can be used for deeper analyses and studies, provided one can count on proper tools to extract relevant information out of them.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, the continuous growth of available computational power 1 has stimulated scientists to move in the direction of high-throughput simulations. [2][3][4][5][6][7][8][9][10] Along this line, open access databases, such as OQMD, 11,12 NOMAD, 13,14 Aflowlib, 15 C2DB, 16,17 QPOD, 18 Materials Project, 19 Materials Cloud, 20 and related AiiDA, 21,22 provide researchers with a huge collection of basic first-principles results. A large amount of ab initio data is thus available, which can be used for deeper analyses and studies, provided one can count on proper tools to extract relevant information out of them.…”
Section: Introductionmentioning
confidence: 99%