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General predictive design strategy for adatom-induced electronic kagome lattices

Develop a general predictive and design strategy, potentially leveraging machine learning, that identifies adatom–substrate combinations which produce electronic kagome lattices upon deposition and reliably forecasts whether a specific adatom–substrate system will yield an electronic kagome band pattern.

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Background

Electronic kagome lattices can form when deposited adatoms create site-dependent electronic states on metal surfaces, as demonstrated for Bi/Au(111), but outcomes depend sensitively on both the adatom and substrate. The field lacks a general predictive framework to determine which combinations will produce electronic kagome bands, hindering systematic exploration and design.

References

However, it depends strongly on the choice of atoms and substrates, and currently neither experiments nor theory can predict which systems will yield electronic kagome lattices. Thus, establishing a general construction strategy, possibly empowered by machine learning models, remains an open question for future exploration.

— Two-dimensional Kagome Materials: Theoretical Insights, Experimental Realizations, and Electronic Structures (2409.03214 - Zhang et al., 5 Sep 2024) in Section 4.2 (Atom-scale electronic kagome lattices), page 29