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Register a new userOrigin of Metal-Insulator Transitions in Correlated Perovskite Metals
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Added by
M. Chandler Bennett
Publication date
2021
fields
Physics
and research's language is
English
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The mechanisms that drive metal-to-insulator transitions (MIT) in correlated solids are not fully understood. For example, the perovskite (PV) SrCoO3 is a FM metal while the oxygen-deficient (n-doped) brownmillerite (BM) SrCoO2.5 is an anti-ferromagnetic (AFM) insulator. Given the magnetic and structural transitions that accompany the MIT, the driver for such a MIT transition is unclear. We also observe that the perovskite metals LaNiO3, SrFeO3, and SrCoO3 also undergo MIT when n-doped via high-to-low valence compositional changes. Also, pressurizing the insulating BM SrCoO2.5 phase, drives a gap closing. Using DFT and correlated diffusion Monte Carlo approaches we demonstrate that the ABO3 perovskites most prone to MIT are self hole-doped materials, reminiscent of a negative charge-transfer system. Upon n-doping away from the negative-charge transfer metallic phase, an underlying charge-lattice (or e-phonon) coupling drives the system to a bond-disproportionated gapped state, thereby achieving ligand hole passivation at certain sites only, leading to charge-disproportionated states. The size of the gap opened is correlated with the size of the hole-filling at these ligand sites. This suggests that the interactions driving the gap opening to realize a MIT even in correlated metals is the charge-transfer energy, but it couples with the underlying phonons to enable the transition to the insulating phase. Other orderings (magnetic, charge, etc.) driven by weaker interactions are secondary and may assist gap openings at small dopings, but its the charge-transfer energy that predominantly determines the bandgap, with a negative energy preferring the metallic phase. This n-doping can be achieved by modulations in stoichiometry or composition or pressure. Hence, controlling the amount of the ligand-hole is key in controlling MIT. We compare our predictions to experiments where possible.
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