Session S04: Condensed Matter III

A simple analog of black hole information paradox in quantum Hall interfaces

The black hole information paradox has been hotly debated for the last few decades, without full resolution. This makes it desirable to find analogs of this paradox in simple and experimentally accessible systems, whose resolutions may shed light on this long-standing and fundamental problem. Here we identify and resolve an apparent “information paradox” in a quantum Hall interface between the Halperin-331 and Pfaffian states. Information carried by pseudospin degree of freedom of the Abelian 331 quasiparticles gets scrambled when they cross the interface to enter non-Abelian Pfaffian state, and becomes inaccessible to local measurements; in this sense the Pfaffian region is an analog of black hole interior while the interface plays a role similar to its horizon. We demonstrate that the “lost” information gets recovered once the “black hole” evaporates and the quasiparticles return to the 331 region, albeit in a highly entangled form. Such recovery is quantified by the Page curve of the entropy carried by these quasiparticles, which are analogs of Hawking radiation.   

Stirring by Staring: Measurement Induced Chirality

Controlling the dynamics of quantum systems is a current frontier of quantum many-body physics. Recent advancements in experimental techniques suggest exciting new directions in drive-induced quantum states. Here, we present a simple scheme that relies solely on occupation measurements to induce a chiral quantum phase. Namely, we show that by utilizing a pattern of repeated quantum measurements we can produce chiral edge transport of fermions hopping on a Lieb lattice. We study in detail the dependence on measurement frequency, showing that in the Zeno limit the system can be described by a classical stochastic dynamics, yielding protected transport. As the frequency of measurements is reduced, the charge flow is reduced and vanishes when no measurements are done.   

Using MuSR to Find Hydrogen-Like Defects in Cadmium Oxide

Muons are particles which were originally found in solar rays, along with pions and other particles. These pions, in the solar rays, decay into muons. Muons can be spin polarized by shooting them at a target. In doing so, the spins of the muons become polarized due to conservation of linear and angular momentum. Once polarized, these muons can be implanted into a sample where their spins will interact with the local environment of the sample. This method of muon implanting was used to collect MuSR (muon spin relaxation) data of implanted muons in Cadmium Oxide. This data was analyzed to determine how many muon sites are in the Cadmium Oxide lattice and the charge of the muon sites. The barrier energy between sites and the ionization energy of the neutral Muonium were also determined. This information can be compared with already published hydrogen modeling to determine the likely locations of the muon sites within the lattice.   

Influence of Mn substitution on the topological metal Zr${_2}$Te${_2}$P

The ternary tetradymite Zr${_2}$Te${_2}$P has been shown to host an unusual band structure, including (i) conventional electronic bands, (ii) a Dirac point at 400 meV above the $\Gamma$ point, and (iii) a Dirac nodal arc at 700 meV below the M point[1,2]. This invites further studies to access these novel bands (e.g., by adjusting the Fermi energy) and to introduce additional interactions (e.g., through chemical substitution of magnetic ions). Here we present results from a study where a small concentration of Mn is substituted into Zr${_2}$Te${_2}$P. X-ray diffraction and chemical analysis (EDS) measurements show that the crystals formed in expected structure and the Mn is present in concentrations of a few percent. Magnetization measurements reveal Curie Weiss behavior that is consistent with the Mn ions being in the divalent state. Fits to the data also indicate a ferromagnetic spin exchange along the c-axis and antiferromagnetic exchange in the ab plane, that is likely mediated by the RKKY interaction. At low temperatures we find evidence for a bulk disordered spin-glass phase, which is evident in the magnetic susceptibility, heat capacity, and electrical resistivity. [1] K.W chen., et al. Phys. Rev. B 97, 165112 (2018). [2] J. Dai., et al. Phys. Rev. Lett 126, 196407 (2021)   

High-Throughput Screening of Semiconductors for Artificial Photosynthesis with Data-Mining and First Principles Calculations

We propose an algorithm for efficient design of semiconductor structures with a selected set of physical properties. We deploy our algorithm to produce semiconductor candidates for artificial photosynthesis, i.e. photocatalytic water splitting. Our candidate structures are composed of earth-abundant elements, capable of trapping sunlight, suitable for H$_2$ and/or O$_2$ production, and stable to reduction and oxidation in aqueous media. First, we predict thousands of undiscovered semiconductors compositions using an ionic translation model trained on a large experimental database. Then, we screen the predicted semiconductors compositions for redox stability under HER or OER conditions. Finally, we generate thermodynamically stable crystal structures and calculate accurate band gap values for these compounds. Ultimately we produce dozens of promising semiconductor candidates with ideal properties for artificial photosynthesis.   

Machine Learning and First-Principles Predictions of Materials with Low Lattice Thermal Conductivity

Data-driven machine learning (ML) approaches have recently become popular and powerful in materials discovery. Here, we use combined ML and density functional theory (DFT) simulations to search for materials with low lattice thermal conductivity, which is crucial for improving the energy conversion performance of thermoelectric devices. Several compounds formed by cadmium as well as elements from the alkali metal and carbon groups are predicted to exhibit low lattice thermal conductivity (< 1 W/mK). Our DFT calculations of electronic structures and transport properties further indicate that the figure of merit ZT value for thermoelectric performance can be greater than 1 near 400 K in compounds like K2CdPb, where are thereby promising candidate materials for low-temperature thermoelectric applications.   

Relating Debye and Superconducting Transition Temperatures by Machine Learning in Conventional Superconductors

Recently a relationship between the Debye temperature $\Theta_D$ and the superconducting transition temperature $T_c$ of conventional superconductors has been proposed [in npj Quantum Materials $\mathbf{3}$, 59 (2018)]. The relationship indicates that for phonon-mediated BCS superconductors the maximum $T_c$ is at most $\sim 0.1\times \Theta_D$. In order to verify this bound and develop tools for predicting the Debye temperature, we trained Machine Learning models on over 10000 compounds with just chemical formula and crystal system information as features. By examining 5000 known superconducting compounds in the NIMS SuperCon database, our predictions show that the conventional superconductors in the database indeed follow the previously proposed bound of $T_c$ vs. $\Theta_D$. We also discuss our manual selection criteria and Machine Learning clustering techniques to separate conventional superconductors from others in the NIMS SuperCon database.