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Title: PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators

Point defects have a strong impact on the performance of semiconductor and insulator materials used in technological applications, spanning microelectronics to energy conversion and storage. The nature of the dominant defect types, how they vary with processing conditions, and their impact on materials properties are central aspects that determine the performance of a material in a certain application. This information is, however, difficult to access directly from experimental measurements. Consequently, computational methods, based on electronic density functional theory (DFT), have found widespread use in the calculation of point-defect properties. Here we have developed the Python Charged Defect Toolkit (PyCDT) to expedite the setup and post-processing of defect calculations with widely used DFT software. PyCDT has a user-friendly command-line interface and provides a direct interface with the Materials Project database. This allows for setting up many charged defect calculations for any material of interest, as well as post-processing and applying state-of-the-art electrostatic correction terms. Our paper serves as a documentation for PyCDT, and demonstrates its use in an application to the well-studied GaAs compound semiconductor. As a result, we anticipate that the PyCDT code will be useful as a framework for undertaking readily reproducible calculations of charged point-defect properties, and thatmore » it will provide a foundation for automated, high-throughput calculations.« less
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [3] ;  [3] ;  [1] ;  [4]
  1. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Univ. catholique de Louvain, Louvain-la-Neuve (Belgium)
Publication Date:
Report Number(s):
PNNL-SA-133200
Journal ID: ISSN 0010-4655; PII: S0010465518300079; TRN: US1801503
Grant/Contract Number:
AC02-05CH11231; 56909; AC05-76RL01830
Type:
Accepted Manuscript
Journal Name:
Computer Physics Communications
Additional Journal Information:
Journal Volume: 226; Journal ID: ISSN 0010-4655
Publisher:
Elsevier
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; point defects; charged defects; semiconductors; insulators; density functional theory; python
OSTI Identifier:
1420872

Broberg, Danny, Medasani, Bharat, Zimmermann, Nils E. R., Yu, Guodong, Canning, Andrew, Haranczyk, Maciej, Asta, Mark, and Hautier, Geoffroy. PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators. United States: N. p., Web. doi:10.1016/J.CPC.2018.01.004.
Broberg, Danny, Medasani, Bharat, Zimmermann, Nils E. R., Yu, Guodong, Canning, Andrew, Haranczyk, Maciej, Asta, Mark, & Hautier, Geoffroy. PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators. United States. doi:10.1016/J.CPC.2018.01.004.
Broberg, Danny, Medasani, Bharat, Zimmermann, Nils E. R., Yu, Guodong, Canning, Andrew, Haranczyk, Maciej, Asta, Mark, and Hautier, Geoffroy. 2018. "PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators". United States. doi:10.1016/J.CPC.2018.01.004. https://www.osti.gov/servlets/purl/1420872.
@article{osti_1420872,
title = {PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators},
author = {Broberg, Danny and Medasani, Bharat and Zimmermann, Nils E. R. and Yu, Guodong and Canning, Andrew and Haranczyk, Maciej and Asta, Mark and Hautier, Geoffroy},
abstractNote = {Point defects have a strong impact on the performance of semiconductor and insulator materials used in technological applications, spanning microelectronics to energy conversion and storage. The nature of the dominant defect types, how they vary with processing conditions, and their impact on materials properties are central aspects that determine the performance of a material in a certain application. This information is, however, difficult to access directly from experimental measurements. Consequently, computational methods, based on electronic density functional theory (DFT), have found widespread use in the calculation of point-defect properties. Here we have developed the Python Charged Defect Toolkit (PyCDT) to expedite the setup and post-processing of defect calculations with widely used DFT software. PyCDT has a user-friendly command-line interface and provides a direct interface with the Materials Project database. This allows for setting up many charged defect calculations for any material of interest, as well as post-processing and applying state-of-the-art electrostatic correction terms. Our paper serves as a documentation for PyCDT, and demonstrates its use in an application to the well-studied GaAs compound semiconductor. As a result, we anticipate that the PyCDT code will be useful as a framework for undertaking readily reproducible calculations of charged point-defect properties, and that it will provide a foundation for automated, high-throughput calculations.},
doi = {10.1016/J.CPC.2018.01.004},
journal = {Computer Physics Communications},
number = ,
volume = 226,
place = {United States},
year = {2018},
month = {2}
}