Modelling energy surfaces of defects in solids

First-principles simulations of atomic and electronic structure in solids offer a powerful route to predict and understand their 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 renewed appreciation for the potential complexity of defect energy surfaces, and the significant impact that metastabilities can have on predicted and observed behaviour.^2–5 I will discuss our work in these areas, including the development of global optimisation strategies, case studies where this behaviour is crucial to macroscopic properties (such as charge compensation and carrier recombination), and remaining challenges in this area.^6,7 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.^8,9



1. Freysoldt, C. et al. First-principles calculations for point defects in solids. Rev. Mod. Phys (2014).

2. Arrigoni, M. & Madsen, G. K. H. Evolutionary computing and machine learning for discovering of low-energy defect configurations. npj Comput Mater (2021).

3. Kononov, A., Lee, C.-W., Shapera, E. P. & Schleife, A. Identifying native point defect configurations in α-alumina. J. Phys.: Condens. Matter (2023).

4. Mosquera-Lois, I. & Kavanagh, S. R. In search of hidden defects. Matter (2021).

5. Mosquera-Lois, I., Kavanagh, S. R., Walsh, A. & Scanlon, D. O. ShakeNBreak: Navigating the defect configurational landscape. Journal of Open Source Software (2022).

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

7. Mosquera-Lois, I., Kavanagh, S. R., Walsh, A. & Scanlon, D. O. Identifying the ground state structures of point defects in solids. npj Comput Mater (2023).

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

10. Kavanagh, S. R., Identifying Split Vacancies using Foundational Machine Learning Models. In Submission.