Machine-Learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces
Frederico P. Delgado, Frederico Sim\~oes, Leeor Kronik, Waldemar Kaiser, David A. Egger

TL;DR
This study uses machine learning-accelerated first-principles calculations to show that halide perovskite surfaces predominantly host shallow electronic states, explaining their defect tolerance and high device performance.
Contribution
The paper introduces a machine learning approach to efficiently analyze electronic states on halide perovskite surfaces, revealing their tendency to host shallow states despite atomic dynamics.
Findings
Approximately 70% of surface states are within 0.2 eV of the valence band edge.
Deep electronic traps are unlikely due to energetic mixing with shallow states.
Atomic dynamics prevent the formation of deep electronic states at surfaces.
Abstract
The spectacular performance of halide perovskites in optoelectronic devices is rooted in their tolerance to defects. Previous studies showed that defects in these materials generate shallow electronic states. However, how these shallow states persist amid the pronounced atomic dynamics on halide perovskite surfaces remains unknown. This work reveals that electronic states at surfaces of prototypical CsPbBr are energetically distributed at room temperature akin to well-passivated inorganic semiconductors, even when covalent bonds remain cleaved and undercoordinated. Specifically, a striking tendency for shallow surface states is found with approximately 70% of surface-state energies appearing within 0.2 eV or from the valence-band edge. While these findings do not rule out occurrence of deep traps per se, they show that even when surface states appear deeper…
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Taxonomy
TopicsPerovskite Materials and Applications · Machine Learning in Materials Science · Electronic and Structural Properties of Oxides
