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Mesaros, A.

Publications and source records attributed to Mesaros, A..

Hund’s coupling mediated multi-channel quantum phase transition of a single magnetic impurity in Fe(Se, Te)

Understanding the interplay between individual magnetic impurities and superconductivity is crucial for bottom-up construction of novel phases of matter. Sub-gap bound states that are used in this endeavor are typically considered as independent entities that each result from the exchange scattering between the respective impurity orbitals and electrons of the superconducting condensate. Here we present experimental evidence of individual multi-spin impurities where the sub-gap states are not independent. Specifically, we find that by tuning the energy of the state closest to zero through zero, all other sub-gap states change particle-hole asymmetry as well. We show that this can be understood by including Hund’s coupling, which favors high-spin configurations, into a multi-orbital Anderson model. Unlike for the case of independent spins, the transition we observe signals the simultaneous departure of more than one quasiparticle from the impurity, while the parity of the ground state may remain unchanged. Our results show that Hund’s coupling is not only crucial in generating high-spin impurities, but also to understand the transition between two distinct ground states, and should therefore be taken into account for e.g. impurity-based band-structure engineering.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Machine learning in electronic-quantum-matter imaging experiments

For centuries, the scientific discovery process has been based on systematic human observation and analysis of natural phenomena. Today, however, automated instrumentation and large-scale data acquisition are generating datasets of such large volume and complexity as to defy conventional scientific methodology. Radically different scientific approaches are needed, and machine learning (ML) shows great promise for research fields such as materials science. Given the success of ML in the analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is to apply this approach to experimental data—for example, to the arrays of complex electronic-structure images17 obtained from atomic-scale visualization of EQM. Here we report the development and training of a suite of artificial neural networks (ANNs) designed to recognize different types of order hidden in such EQM image arrays. These ANNs are used to analyse an archive of experimentally derived EQM image arrays from carrier-doped copper oxide Mott insulators. In these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs determine that this state is unidirectional, revealing a coincident nematic EQM state. Here, strong-coupling theories of electronic liquid crystals are consistent with these observations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗