Recording of my invited seminar for the Université de Lille TimeMan seminar series: https://pod.univ-lille.fr/video/33615... – discussing the issue of global optimisation for defects in solids and our work (ShakeNBreak) on tackling this issue.
Thanks for having me!
ShakeNBreak: https://shakenbreak.readthedocs.io
doped: https://doped.readthedocs.io
Theory/Method paper: https://www.nature.com/articles/s4152...
Check out the other talks on my YouTube channel for more on defect calculations in materials!
For other research articles see:
https://bit.ly/3pBMxOG
Other references:
Matter Preview of Defect Structure Searching: https://www.sciencedirect.com/science...
Metastable defects : https://doi.org/10.1039/D2FD00043A
Recombination at V_Cd in CdTe (case study): https://pubs.acs.org/doi/abs/10.1021/...
Abstract:
Defects are a universal, unavoidable feature of solid materials, controlling properties and performance in most functional devices (including solar cells, LEDs, transistors, thermoelectrics and batteries). Recent years have seen a dramatic acceleration in the design and development of functional materials, thanks in many parts to powerful combinations of experimental measurements and computational modelling predictions.
The standard approach of modelling defects is, however, prone to obtaining inaccurate results in certain cases. This can occur when the atoms around the defect rearrange in a way that lowers their energy, which can be difficult to predict and identify with our current state-of-the-art computational methods. These rearranged defects can be crucial to the overall behaviour and effect on the material properties, hindering the accuracy (and effectiveness) of defect research. While this issue has been witnessed as far back as 4 decades ago, and attempts made to tackle it, a general and affordable method to account for this behaviour has eluded researchers until now.
In this work, we develop a novel method to efficiently ‘search’ for these defect rearrangements, involving targeted atomic bond distortions, and demonstrate its effectiveness, generality and affordability by applying it to a wide range of solid materials (from the prototype semiconductor silicon, to industry-relevant insulators and ionic oxides such as cerium dioxide, and many others in-between). We show that these reconstructions are more common than previously believed, and that they can be crucial to material performance. We have developed a computational program to implement our method (ShakeNBreak), and are confident it will provide a significant step forward for accurate quantitative modelling of defects in solids.
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2. Chadi, D. J. & Chang, K. J. Energetics of DX-center formation in GaAs and ${\mathrm{Al}}_{\mathrm{x}}$${\mathrm{Ga}}_{1\mathrm{\ensuremath{-}}\mathrm{x}}$As alloys. Phys. Rev. B 39, 10063–10074 (1989).
3. Morris, A. J., Pickard, C. J. & Needs, R. J. Hydrogen/silicon complexes in silicon from computational searches. Phys. Rev. B 78, 184102 (2008).
4. Arrigoni, M. & Madsen, G. K. H. Evolutionary computing and machine learning for discovering of low-energy defect configurations. npj Comput Mater 7, 1–13 (2021).
5. Mosquera-Lois, I. & Kavanagh, S. R. In search of hidden defects. Matter 4, 2602–2605 (2021).
6. Mosquera-Lois, I., Kavanagh, S. R., Walsh, A. & Scanlon, D. O. Identifying the ground state structures of point defects in solids. npj Comput Mater 9, 1–11 (2023).
7. Mannodi-Kanakkithodi, A. The devil is in the defects. Nat. Phys. 19, 1243–1244 (2023).
8. Mosquera-Lois, I., Kavanagh, S. R., Walsh, A. & Scanlon, D. O. ShakeNBreak: Navigating the defect configurational landscape. Journal of Open Source Software 7, 4817 (2022).
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