  On semiconductor growth surfaces, surface reconstructions appear. Estimation
of the reconstructed structures is essential for understanding and controlling
growth phenomena. In this study, the stability of a mixture of two different
surface reconstructions is investigated. Since the number of candidate
structures is enormous, the structures sampled by Bayesian optimization are
analyzed. As a result, the local electron counting (EC) rule alone was found to
be insufficient to explain such stability. Then, augmenting the EC rule, a
data-driven Ising model is proposed. The model allows the evaluation of the
whole enormous number of candidate structures. The approach is expected to be
useful for theoretical studies of such mixtures on various semiconductor
surfaces.
