Abstract

First-principles simulations of atomic and electronic structure in solids offer a powerful route to predict and understand material properties.[1] This is particularly relevant in the case of point defects which dramatically affect material properties yet present many challenges for experimental characterisation.

Recent years have seen significant advances in both computational methodologies[1,2] and associated toolkits[3–5] for modelling defect behaviour. I will briefly discuss my collaborative efforts in this area, including approaches for modelling defect metastabilities and their effects on electron-hole recombination,[6–8] the development of the open-source doped defect simulation package,[3] and remaining challenges in this area.[9] Lastly, I will discuss recent work on extending these approaches using foundational machine-learning models, demonstrating exciting potential for these methods in the field of defect modelling, but with important caveats regarding their accuracy and reliability at present.[10,11]

1. 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).

2. Alkauskas, A., Yan, Q. & Van de Walle, C. G. First-principles theory of nonradiative carrier capture via multiphonon emission. Phys. Rev. B 90 , 075202 (2014).

3. Kavanagh, S. R. et al. doped: Python toolkit for robust and repeatable charged defect supercell calculations. Journal of Open Source Software 9, 6433 (2024).

4. Huang, M. et al. DASP : Defect and Dopant ab-initio Simulation Package. J. Semicond. 43, 042101 (2022).

5. 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).

6. Kavanagh, S. R., Walsh, A. & Scanlon, D. O. Rapid Recombination by Cadmium Vacancies in CdTe. ACS Energy Lett. 6, 1392–1398 (2021).

7. Kavanagh, S. R., Scanlon, D. O., Walsh, A. & Freysoldt, C. Impact of metastable defect structures on carrier recombination in solar cells. Faraday Discuss. 239, 339–356 (2022).

8. Wang, X., Kavanagh, S. R., Scanlon, D. O. & Walsh, A. Upper efficiency limit of Sb 2 Se 3 solar cells. Joule 8, 2105–2122 (2024).

9. Squires, A. G., Kavanagh, S. R., Walsh, A. & Scanlon, D. O. Guidelines for robust and reproducible point defect simulations in crystals. Nat Rev Mater 1–18 (2026) doi:10.1038/s41578-025-00879-y.

10. Mosquera-Lois, I., Kavanagh, S. R., Ganose, A. M. & Walsh, A. Machine-learning structural reconstructions for accelerated point defect calculations. npj Comput Mater 10, 1–9 (2024).

11. Kavanagh, S. R. Identifying split vacancy defects with machine-learned foundation models and electrostatics. J. Phys. Energy 7, 045002 (2025).