Doped: A Python Package for Solid-State Defect and Dopant Calculations

When and Where

Nov 29, 2023
8:00pm - 10:00pm

Hynes, Level 1, Hall A

Presenter

Seán Kavanagh

Aron Walsh

David Scanlon

Co-Author(s)

Seán Kavanagh^1,2,Aron Walsh¹,David Scanlon³

Imperial College London¹,University College London²,University of Birmingham³

Abstract

Seán Kavanagh^1,2,Aron Walsh¹,David Scanlon³

Imperial College London¹,University College London²,University of Birmingham³

Point defects are a universal feature of crystalline materials, dictating the functionality of most semiconductor materials, such as microelectronics, solar cells and batteries.1 The major impact, despite minute concentrations, of defects in solids, renders their identification and characterisation by experiment extremely challenging. Thus, computational methods (typically Density Functional Theory (DFT) but also quantum embedding, GW and empirical potentials approaches) are widely used to predict defect behavior in solids, before combining and comparing theoretical predictions with experimental measurements. However, there are many critical stages in the computational workflow for defects in solids, which, when performed manually, not only leave room for human error but also consume significant time and effort of the researcher. Here we report doped, our python package for the full generation, calculation setup, post-processing and analysis of defect supercell calculations.2–5 The generation and thermodynamic analysis (i.e. defect formation energy diagrams, chemical potentials & stability region, doping analysis etc.) are agnostic to the underlying first-principles software, while input file generation is supported for several of the most widely-used DFT codes, including VASP, FHI-aims, CP2k, Quantum Espresso and CASTEP. Moreover, doped is built to be compatible with other computational toolkits for advanced defect characterisation, including ShakeNBreak6 for defect structure-searching, py-sc-fermi7 for in-depth concentration and Fermi level analysis, and CarrierCapture.jl8/nonrad9 for non-radiative recombination calculations. Its object-oriented python framework make it readily-usable in high-throughput architectures such as atomate(2) or AiiDA, with examples included in the documentation. We will discuss the key features of doped for computational defect workflows, exemplified with relevant systems (CdTe, t-Se, Y2Ti2O5S2). We anticipate that doped will serve as a highly useful tool for computational defect researchers, being an efficient platform for conducting reproducible calculations of solid-state defect properties. 1 D. Broberg, B. Medasani, N. E. R. Zimmermann, G. Yu, A. Canning, M. Haranczyk, M. Asta and G. Hautier, Computer Physics Communications, 2018, 226, 165–179. 2 S. R. Kavanagh, A. Walsh and D. O. Scanlon, ACS Energy Lett., 2021, 6, 1392–1398. 3 Y.-T. Huang, S. R. Kavanagh, M. Righetto, M. Rusu, I. Levine, T. Unold, S. J. Zelewski, A. J. Sneyd, K. Zhang, L. Dai, A. J. Britton, J. Ye, J. Julin, M. Napari, Z. Zhang, J. Xiao, M. Laitinen, L. Torrente-Murciano, S. D. Stranks, A. Rao, L. M. Herz, D. O. Scanlon, A. Walsh and R. L. Z. Hoye, Nat Commun, 2022, 13, 4960. 4 S. R. Kavanagh, D. O. Scanlon, A. Walsh and C. Freysoldt, Faraday Discuss., 2022, 239, 339–356. 5 A. Nicolson, S. R. Kavanagh, C. N. Savory, G. W. Watson and D. O. Scanlon, 2023. 6 I. Mosquera-Lois, S. R. Kavanagh, A. Walsh and D. O. Scanlon, Journal of Open Source Software, 2022, 7, 4817. 7 A. G. Squires, D. O. Scanlon and B. J. Morgan, Journal of Open Source Software, 2023, 8, 4962. 8 S. Kim, S. N. Hood, P. van Gerwen, L. D. Whalley and A. Walsh, Journal of Open Source Software, 2020, 5, 2102. 9 M. E. Turiansky, A. Alkauskas, M. Engel, G. Kresse, D. Wickramaratne, J.-X. Shen, C. E. Dreyer and C. G. Van de Walle, Computer Physics Communications, 2021, 267, 108056.