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  Parametrically constrained geometry relaxations for high-throughput materials science

Lenz, M.-O., Purcell, T., Hicks, D., Curtarolo, S., Scheffler, M., & Carbogno, C. (2019). Parametrically constrained geometry relaxations for high-throughput materials science. npj Computational Materials, 5: 123. doi:10.1038/s41524-019-0254-4.

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1908.01610.pdf (Preprint), 2MB
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 Creators:
Lenz, Maja-Olivia1, Author           
Purcell, Thomas1, Author           
Hicks, David2, Author
Curtarolo, Stefano2, Author
Scheffler, Matthias1, Author           
Carbogno, Christian1, Author           
Affiliations:
1NOMAD, Fritz Haber Institute, Max Planck Society, ou_3253022              
2Department of Materials Science and Mechanical Engineering, Duke University, Durham, NC 27708, USA, ou_persistent22              

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Free keywords: Condensed Matter, Materials Science, cond-mat.mtrl-sci
 Abstract: Reducing parameter spaces via exploiting symmetries has greatly accelerated and increased the quality of electronic-structure calculations. Unfortunately, many of the traditional methods fail when the global crystal symmetry is broken, even when the distortion is only a slight perturbation (e.g., Jahn-Teller like distortions). Here we introduce a flexible and generalizable parametric relaxation scheme and implement it in the all-electron code FHI-aims. This approach utilizes parametric constraints to maintain symmetry at any level. After demonstrating the method’s ability to relax metastable structures, we highlight its adaptability and performance over a test set of 359 materials, across 13 lattice prototypes. Finally we show how these constraints can reduce the number of steps needed to relax local lattice distortions by an order of magnitude. The flexibility of these constraints enables a significant acceleration of high-throughput searches for novel materials for numerous applications.

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Language(s): eng - English
 Dates: 2019-08-052019-08-032019-11-122019-12-17
 Publication Status: Published online
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Project name : TEC1p - Big-Data Analytics for the Thermal and Electrical Conductivity of Materials from First Principles
Grant ID : 740233
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: npj Computational Materials
  Abbreviation : npj Comput. Mater.
Source Genre: Journal
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Publ. Info: London : Springer Nature
Pages: 19 Volume / Issue: 5 Sequence Number: 123 Start / End Page: - Identifier: ISSN: 2057-3960
CoNE: https://pure.mpg.de/cone/journals/resource/2057-3960