Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics

Understand

Point defects are ubiquitous in solid-state compounds, dictating many functional properties such as conductivity, catalytic activity and carrier recombination.

• Over the past decade, the prevalence of metastable defect geometries and their importance to relevant properties has been increasingly recognised.
• A striking example is split vacancies, where an isolated atomic vacancy transforms to a stoichiometry-conserving complex of two vacancies and an interstitial ($V_X \rightarrow [V_X + X_i + V_X]$), which can be accompanied by a dramatic energy lowering and change in behaviour.
• These species are particularly challenging to identify from computation, due to the `non-local' nature of this reconstruction.