Eun-Ah Kim

The Bragg glass phase is a nearly perfect crystal with glassy features predicted to occur in vortex lattices and charge-density-wave systems in the presence of disorder. Detecting it has been challenging, despite its sharp theoretical definition in terms of diverging correlation lengths. Here we present bulk probe evidence supporting a Bragg glass phase in the systematically disordered charge-density-wave material of PdxErTe3. We do this by using comprehensive X-ray data and a machine-learning-based analysis tool called X-ray diffraction temperature clustering (X-TEC). We establish a diverging correlation length in samples with moderate intercalation over a wide temperature range. To enable this analysis, we introduced a high-throughput measure of inverse correlation length that we call peak spread. The detection of Bragg glass order and the resulting phase diagram advance our understanding of the complex …

With the promising development of quantum computers comes a natural necessity of storing quantum information, which can be itself a challenging task due to the omnipresence of noise induced errors in quantum devices. Quantum error-correcting codes aim to combat this issue by encoding logical qubits in the state space of a large number of physical qubits, so that within a certain threshold of noise strength, faithful reconstruction of the stored logical qubit is still possible. However, the decoding scheme often depends on specific quantum code and noise models, making it a challenging task in practice. Using the machinery of the quantum attention networks (QAN), we study the phase diagram of error correcting codes, under coherent and incoherent perturbations. We find the network learns to identify the error-protected, long-range-entangled phase, by acting as an effective neural-network based decoder that …

In the last few years, large language models (LLMs) have exhibited an unprecedented ability to perform complex linguistic and reasoning tasks. To date, evaluation of scientific and mathematical reasoning ability in LLMs has been limited to simplistic pedagogical tasks, often algorithmically extracted. However, there are no existing evaluations of the ability of LLMs to understand and solve multi-step graduate-level calculations within physics, skills that are critical prerequisites to performing advanced physics research. To close this gap, we produce a novel dataset of multi-step Hartree-Fock many-body physics calculations through annotation of a well-defined class of condensed matter theory papers. The dataset distills each paper into a sequence of prompt-answer pairs. We first evaluated the ability of LLMs to extract relevant information from research papers and aid in the construction of the prompts. We then …

G50. 00006: Attention to complexity I: witnessing the entanglement phase transition with attention-based neural networks*

The advent of expansive material databases provides an unprecedented opportunity to investigate predictive descriptors for emergent material properties. Particularly exciting possibility is to use expertly curated data to learn the descriptor as mechanism to bottle the human expert reasoning and intuition. For this, a reliable measurement based and expertly curated data and bench marking insight by expert researcher are critical. As a first step towards such program, we focus on the topological semi metal (TSM) among square-net materials as target property, inspired by the expert identified descriptor based on structural information: the tolerance factor [1]. We start by curating a dataset encompassing 12 primary features of 879 square-net materials, using experimental data whenever possible. We then use Dirichlet-based Gaussian process regression [2] using a specialized kernel [3] to reveal composite descriptors …

We study the stability of charge order in the kagome metal ScV6Sn6 using synchrotron x-ray diffraction (XRD) measurements. XRD data reveal high-temperature, short-range charge correlations at the wave vector of q=(1/3, 1/3, 1/2) whose inter-layer correlation lengths diverge upon cooling. At the charge order transition, this divergence is interrupted and long-range order freezes at the wave vector of q=(1/3, 1/3, 1/3), while disorder enables the charge correlations to persist at the q=(1/3, 1/3, 1/2) wave vector down to the lowest temperatures measured. Both long-range and short-range charge correlations seemingly arise from the same instability and both are rapidly quenched upon the doping of larger Y ions onto the Sc sites. Our results validate the theoretical prediction of the primary lattice instability at q=(1/3, 1/3, 1/2), and we present a qualitative picture of the frustration of charge order in this compound.

Recent experiments on hole-doped Bernal bilayer graphene under strong displacement field discovered that two seemingly separate knobs--in-plane magnetic field or proximity spin-orbit coupling--promote superconductivity. Without these knobs, a competing phase featuring high resistivity and non-linear charge transport reminiscent of the charge density wave depinning appears instead. While previous works pointed out various possible mechanisms for the superconductivity, the key question of how two separate knobs are promoting superconductivity as well as the nature of the competing phase have not been clear. Here, we study instabilities arising from repulsive interactions near van Hove singularities in Bernal bilayer graphene through parquet renormalization group. We note that both the in-plane field and the proximity spin-orbit coupling have the effect of lifting the spin degeneracy. This observation opens …

The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for quantum simulation that can be run on near-term quantum hardware. A challenge in VQE -- as well as any other heuristic algorithm for finding ground states of Hamiltonians -- is to know how close the algorithm's output solution is to the true ground state, when the true ground state and ground-state energy are unknown. This is especially important in iterative algorithms, such as VQE, where one wants to avoid erroneous early termination. Recent developments in Hamiltonian reconstruction -- the inference of a Hamiltonian given an eigenstate -- give a metric can be used to assess the quality of a variational solution to a Hamiltonian-eigensolving problem. This metric can assess the proximity of the variational solution to the ground state without any knowledge of the true ground state or ground-state energy. In numerical simulations and in demonstrations on a cloud-based trapped-ion quantum computer, we show that for examples of both one-dimensional transverse-field-Ising (11 qubits) and two-dimensional J1-J2 transverse-field-Ising (6 qubits) spin problems, the Hamiltonian-reconstruction distance gives a helpful indication of whether VQE has yet found the ground state or not. Our experiments included cases where the energy plateaus as a function of the VQE iteration, which could have resulted in erroneous early stopping of the VQE algorithm, but where the Hamiltonian-reconstruction distance correctly suggests to continue iterating. We find that the Hamiltonian-reconstruction distance has a useful correlation with the fidelity between the VQE solution and …

Geometric frustration is a phenomenon in a lattice system where not all interactions can be satisfied, the simplest example being antiferromagnetically coupled spins on a triangular lattice. Frustrated systems are characterized by their many nearly degenerate ground states, leading to non-trivial phases such as spin ice and spin liquids. To date most studies are on geometric frustration of spins; much less explored is orbital geometric frustration. For electrons in twisted bilayer graphene (tBLG) at denominator 3 fractional filling, Coulomb interactions and the Wannier orbital shapes are predicted to strongly constrain spatial charge ordering, leading to geometrically frustrated ground states that produce a new class of correlated insulators (CIs). Here we report the observation of dominant denominator 3 fractional filling insulating states in large angle tBLG; these states persist in magnetic fields and display magnetic ordering signatures and tripled unit cell reconstruction. These results are in agreement with a strong-coupling theory of symmetry-breaking of geometrically frustrated fractional states.

Large language models (LLMs) have demonstrated an unprecedented ability to perform complex tasks in multiple domains, including mathematical and scientific reasoning. We demonstrate that with carefully designed prompts, LLMs can accurately carry out key calculations in research papers in theoretical physics. We focus on a broadly used approximation method in quantum physics: the Hartree-Fock method, requiring an analytic multi-step calculation deriving approximate Hamiltonian and corresponding self-consistency equations. To carry out the calculations using LLMs, we design multi-step prompt templates that break down the analytic calculation into standardized steps with placeholders for problem-specific information. We evaluate GPT-4's performance in executing the calculation for 15 research papers from the past decade, demonstrating that, with correction of intermediate steps, it can correctly derive the final Hartree-Fock Hamiltonian in 13 cases and makes minor errors in 2 cases. Aggregating across all research papers, we find an average score of 87.5 (out of 100) on the execution of individual calculation steps. Overall, the requisite skill for doing these calculations is at the graduate level in quantum condensed matter theory. We further use LLMs to mitigate the two primary bottlenecks in this evaluation process: (i) extracting information from papers to fill in templates and (ii) automatic scoring of the calculation steps, demonstrating good results in both cases. The strong performance is the first step for developing algorithms that automatically explore theoretical hypotheses at an unprecedented scale.

Low valence nickelates are a close analog to cuprates, sharing structural and electronic motifs and hosting superconductivity and charge density wave (CDW) phases in close proximity. Whether these correlated phases have common origins in both systems is an open question. While static CDWs are thought to compete with superconductivity, CDW fluctuations could enhance or even contribute to superconducting pairing [1]. We use non-resonant ultrafast x-ray diffraction to observe the suppression and subsequent recovery of the CDW phase in the stripe-ordered trilayer nickelate La 4 Ni 3 O 8 [2] in response to an 800 nm pump excitation. The intensity of the (-1/3, 1/3, 9) CDW peak was monitored with a 2D detector, simultaneously measuring the response along a 2D momentum cut through the CDW peak. Conventional analysis required large time and momentum binning, due to weak CDW intensity and high …

With rapid progress in simulation of strongly interacting quantum Hamiltonians, the challenge in characterizing unknown phases becomes a bottleneck for scientific progress. We demonstrate that a Quantum-Classical hybrid approach (QuCl) of mining sampled projective snapshots with interpretable classical machine learning can unveil signatures of seemingly featureless quantum states. The Kitaev-Heisenberg model on a honeycomb lattice under external magnetic field presents an ideal system to test QuCl, where simulations have found an intermediate gapless phase (IGP) sandwiched between known phases, launching a debate over its elusive nature. We use the correlator convolutional neural network, trained on labeled projective snapshots, in conjunction with regularization path analysis to identify signatures of phases. We show that QuCl reproduces known features of established phases. Significantly, we …

G50. 00008: Attention to complexity III: learning the complexity of random quantum circuit states*

T09. 00006: Critical behavior of fractionalized excitations in trimer model of twisted bilayer graphene*

Here we study the stability of charge order in the kagome metal ScV 6 Sn 6. Synchrotron x-ray diffraction measurements reveal high-temperature, short-range charge correlations at the wave vectors along q=(1 3, 1 3, 1 2) whose interlayer correlation lengths diverge upon cooling. At the charge order transition, this divergence is interrupted, and long-range order freezes in along q=(1 3, 1 3, 1 3), as previously reported, while disorder enables the charge correlations to persist at the q=(1 3, 1 3, 1 2) wave vector down to the lowest temperatures measured. Both short-range and long-range charge correlations seemingly arise from the same instability and both are rapidly quenched upon the introduction of larger Y ions onto the Sc sites. Our results validate the theoretical prediction of the primary lattice instability at q=(1 3, 1 3, 1 2), and we present a heuristic picture for viewing the frustration of charge order in this compound.

Exploring the phase diagram of the Fermi-Hubbard model is among the key motivations of quantum simulation experiments. We apply the Hybrid-CCNN approach to quantum gas microscopy data of the Fermi Hubbard model across a large doping range. The Hybrid-CCNN approach combines unbiased unsupervised machine learning with feature revealing supervised machine learning. The unsupervised learning stage identifies three different regimes: the magnetic polaron regime at low to intermediate dopings, and the Fermi liquid regime at high dopings, consistent with the manual analysis based on target correlation functions. Moreover, unsupervised learning identifies the cross-over regime as a distinct region of the phase space. The feature analysis using interpretable supervised learning techniques reveals characteristics unique to this intermediate phase. We discuss theoretical implications of the machine …

The kagome superconductors KV 3 Sb 5, RbV 3 Sb 5, and CsV 3 Sb 5 are known to display charge density wave (CDW) order which impacts the topological characteristics of their electronic structure. Details of their structural ground states and how they evolve with temperature are revealed here using single crystal x-ray crystallographic refinements as a function of temperature, carried out with synchrotron radiation. The compounds KV 3 Sb 5 and RbV 3 Sb 5 present 2× 2× 2 superstructures in the F m m m space group with a staggered trihexagonal deformation of vanadium layers. CsV 3 Sb 5 displays more complex structural evolution, whose details have been unravelled by applying machine learning methods to the scattering data. Upon cooling through the CDW transition, CsV 3 Sb 5 displays a staged progression of ordering from a 2× 2× 1 supercell and a 2× 2× 2 supercell into a final 2× 2× 4 supercell that …

Fractionalization without time-reversal symmetry breaking is a long-sought-after goal in the study of correlated phenomena. The earlier proposal of correlated insulating states at n±1/3 filling in twisted bilayer graphene and recent experimental observations of insulating states at those fillings strongly suggest that moiré graphene systems provide a new platform to realize time-reversal symmetric fractionalized states. However, the nature of fractional excitations and the effect of quantum fluctuation on the fractional correlated insulating states are unknown. We show that excitations of the fractional correlated insulator phases in the strong coupling limit carry fractional charges and exhibit fractonic restricted mobility. Upon introduction of quantum fluctuations, the resonance of “lemniscate” structured operators drives the system into quantum lemniscate liquid (QLL) or quantum lemniscate solid (QLS). We find an emergent …

Generalized Wigner Crystal (GWC) is a novel quantum phase of matter driven by further-range interaction at fractional fillings of a lattice. The role of further range interaction as the driver for the incompressible state is akin to Wigner crystal. On the other hand, the significant role of commensurate filling is akin to the Mott insulator. Recent progress in simulator platforms presents unprecedented opportunities to investigate quantum melting in the strongly interacting regime through synergy between theory and experiments. However, the earlier theory literature presents diverging predictions. We study the quantum freezing of GWC through large-scale density matrix renormalization group simulations of a triangular lattice extended Hubbard model. We find a single first-order phase transition between the Fermi liquid and the GWC state. The GWC state shows long-range antiferromagnetic N{\'e}el order. Our results present the simplest answers to the question of the quantum phase transition into the GWC phase and the properties of the GWC phase.

Various data-driven attempts with classical agents have been made to analyze and capture the properties of quantum circuit, primarily to understand the region of quantum supremacy. Here, we also introduce a data-driven supervised-learning approach for our question of the quantum complexity of the given quantum circuit outputs. In particular, we focus on the transformer-based neural network model, consisting of several attention modules. With the conjecture that single circuit output will not solely demonstrate system property, a set of measured bit-strings from the wave function is fed into the neural network. As the experimental circuit data has a high level of noise, we first focus on the noiseless data to see whether there is a significant signal in the circuit data. We present a model performance on depth classification with varying noise levels, further discussing the possibility of quantum complexity …

Machine learning has recently emerged as a promising approach for studying complex phenomena characterized by rich datasets. In particular, data-centric approaches lead to the possibility of automatically discovering structures in experimental datasets that manual inspection may miss. Here, we introduce an interpretable unsupervised-supervised hybrid machine learning approach, the hybrid-correlation convolutional neural network (hybrid-CCNN), and apply it to experimental data generated using a programmable quantum simulator based on Rydberg atom arrays. Specifically, we apply hybrid-CCNN to discover and identify new quantum phases on square lattices with programmable interactions. The initial unsupervised dimensionality reduction and clustering stage first reveals five distinct quantum phase regions. In a second supervised stage, we refine these phase boundaries and seek insights into the …

With rapid progress in simulation of strongly interacting quantum Hamiltonians, the challenge in characterizing unknown phases becomes a bottleneck for scientific progress. We demonstrate that a Quantum-Classical hybrid approach (QuCl) of mining the projective snapshots with interpretable classical machine learning, can unveil new signatures of seemingly featureless quantum states. The Kitaev-Heisenberg model on a honeycomb lattice with bond-dependent frustrated interactions presents an ideal system to test QuCl. The model hosts a wealth of quantum spin liquid states: gapped and gapless spin liquids, and a chiral spin liquid (CSL) phase in a small external magnetic field. Recently, various simulations have found a new intermediate gapless phase (IGP), sandwiched between the CSL and a partially polarized phase, launching a debate over its elusive nature. We reveal signatures of phases in the model by contrasting two phases pairwise using an interpretable neural network, the correlator convolutional neural network (CCNN). We train the CCNN with a labeled collection of sampled projective measurements and reveal signatures of each phase through regularization path analysis. We show that QuCl reproduces known features of established spin liquid phases and ordered phases. Most significantly, we identify a signature motif of the field-induced IGP in the spin channel perpendicular to the field direction, which we interpret as a signature of Friedel oscillations of gapless spinons forming a Fermi surface. Our predictions can guide future experimental searches for spin liquids.

The advent of material databases provides an unprecedented opportunity to uncover predictive descriptors for emergent material properties from vast data space. However, common reliance on high-throughput ab initio data necessarily inherits limitations of such data: mismatch with experiments. On the other hand, experimental decisions are often guided by an expert's intuition honed from experiences that are rarely articulated. We propose using machine learning to "bottle" such operational intuition into quantifiable descriptors using expertly curated measurement-based data. We introduce "Materials Expert-Artificial Intelligence" (ME-AI) to encapsulate and articulate this human intuition. As a first step towards such a program, we focus on the topological semimetal (TSM) among square-net materials as the property inspired by the expert-identified descriptor based on structural information: the tolerance factor. We start by curating a dataset encompassing 12 primary features of 879 square-net materials, using experimental data whenever possible. We then use Dirichlet-based Gaussian process regression using a specialized kernel to reveal composite descriptors for square-net topological semimetals. The ME-AI learned descriptors independently reproduce expert intuition and expand upon it. Specifically, new descriptors point to hypervalency as a critical chemical feature predicting TSM within square-net compounds. Our success with a carefully defined problem points to the "machine bottling human insight" approach as promising for machine learning-aided material discovery.

Immediately following its first synthesis in the 1980's Ba 3 In 2 O 6 was, due to its structural similarity to La (2-x) Sr x CaCu 2 O 6 and La 2 CaCu 2 O (6+ Δ), hypothesized to be a high-T c superconductor. Ba 3 In 2 O 6 being highly hygroscopic, however, inhibited any characterization of its transport properties. In the following years the material was all but forgotten until recently machine learning predicted its T c to be 45.9 K. To answer the question whether Ba 3 In 2 O 6 is a high T c superconductor, Ba 3 In 2 O 6 films were grown by molecular-beam epitaxy and capped by amorphous SiO 2. The indium species were supplied by a newly developed suboxide source emanating a beam of In 2 O. In the course of successfully growing epitaxial Ba 3 In 2 O 6 films, the previously unknown member of the same Ruddlesden-Popper series, Ba 4 In 2 O 7, was also epitaxially grown. Despite the high quality of our films, which …

Indistinguishability of particles is a fundamental principle of quantum mechanics. For all elementary and quasiparticles observed to date-including fermions, bosons, and Abelian anyons-this principle guarantees that the braiding of identical particles leaves the system unchanged. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well developed mathematical description of non-Abelian anyons and numerous theoretical proposals, their experimental observation has remained elusive for decades. Using a superconducting quantum processor, we prepare the ground state of the surface code and manipulate it via unitary operations to form wavefunctions that are described by non …

Indistinguishability of particles is a fundamental principle of quantum mechanics. For all elementary and quasiparticles observed to date—including fermions, bosons and Abelian anyons—this principle guarantees that the braiding of identical particles leaves the system unchanged,. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions, , , –. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals, , , , , , , , , , , , –, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena …

Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for an optimal interface design. Here we introduce a high-throughput computational framework, InterMatch, for efficiently predicting charge transfer, strain, and superlattice structure of an interface by leveraging the databases of individual bulk materials. Specifically, the algorithm reads in the lattice vectors, density of states, and the stiffness tensors for each material in their isolated form from the Materials Project. From these bulk properties, InterMatch estimates the interfacial properties. We benchmark InterMatch predictions for the charge transfer against experimental measurements and supercell density-functional theory calculations. We then use InterMatch to predict promising interface candidates for doping transition metal …

The increasing volume and complexity of data from modern probes call for new data-centric approaches for connecting the data to scientific insight. 4-dimensional scanning transmission electron microscopy (4D-STEM) data provides complete imaging of the 2D electron diffraction patterns at each spatial pixel, opening access to rich physics of large scale spatial variation in charge distribution and atomic structure. However, the large dimensionality of the dataset, especially when taken under a variation in a control knob such as temperature, quickly overwhelms traditional mode of data analysis. To harness the new information 4D-STEM can offer, we adopt an unsupervised machine-learning technique, X-ray TEmperature series Clustering (X-TEC) recently developed for voluminous X-ray data [1]. We focus on rich charge density wave ordering phenomenology of transition metal dichalcogenide, specifically, 1T-TaS …

The original version of this Article contained an error in the Acknowledgements, which incorrectly read ‘The authors acknowledge support by the NSF [Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials (PARADIM)] under cooperative agreement no. DMR-U638986.’. The correct version states ‘DMR-1539918’in place of ‘DMRU638986’. This has been corrected in both the PDF and HTML versions of the Article.

Stabilizer codes allow for non-local encoding and processing of quantum information. Deformations of stabilizer surface codes introduce new and non-trivial geometry, in particular leading to emergence of long sought after objects known as projective Ising non-Abelian anyons. Braiding of such anyons is a key ingredient of topological quantum computation. We suggest a simple and systematic approach to construct effective unitary protocols for braiding, manipulation and readout of non-Abelian anyons and preparation of their entangled states. We generalize the surface code to a more generic graph with vertices of degree 2, 3 and 4. Our approach is based on the mapping of the stabilizer code defined on such a graph onto a model of Majorana fermions charged with respect to two emergent gauge fields. One gauge field is akin to the physical magnetic field. The other one is responsible for emergence of the non …

The ideal honeycomb lattice, featuring sublattice and SU (2) spin rotation symmetries, is a fundamental model for investigating quantum matter with topology and correlations. With the rise of the moiré-based design of model systems, realizing a tunable and symmetric honeycomb lattice system with a narrow bandwidth can open access to new phases and insights. We propose the ABBA-stacked twisted double bilayer WSe 2 as a realistic and tunable platform for reaching this goal. Adjusting the twist angle allows the bandwidth and the ratio between hopping parameters of different ranges to be tuned. Moreover, the system's small bandwidth and spin rotation symmetry enable effective control of the electronic structure through an in-plane magnetic field. We construct an extended Hubbard model for the system to demonstrate this tunability and explore possible ordered phases using the Hartree-Fock approximation …

Much attention has been brought to the rich phase diagram of the honeycomb Kitaev model, which hosts both gapped and gapless Z2 spin liquids in the exactly solvable regime. Here we ask the question: can data-driven techniques be used to discover features governing phase transitions away from the solvable point of the Kitaev model? We approach DMRG ground states from a quantum measurement perspective and take snapshots by repeatedly sampling projective measurements. We train an interpretable neural network architecture, the correlator convolutional neural network, to discern characteristic features of different phases. The network is designed to process n-point functions of the spin degrees of freedom, picking out the most relevant terms that can be used to distinguish phases. We finally interpret the correlation functions learned by the neural network and relate them to existing understanding of the …

Twisted van der Waals multilayers are widely regarded as a rich platform to access novel electronic phases thanks to the multiple degrees of freedom available for controlling their electronic and chemical properties. Here, we propose that the stacking domains that form naturally due to the relative twist between successive layers act as an additional ”knob” for controlling the behavior of these systems and report the emergence and engineering of stacking domain-dependent surface chemistry in twisted few-layer graphene. Using mid-infrared near-field optical microscopy and atomic force microscopy, we observe a selective adhesion of metallic nanoparticles and liquid water at the domains with rhombohedral stacking configurations of minimally twisted double bi- and trilayer graphene. Furthermore, we demonstrate that the manipulation of nanoparticles located at certain stacking domains can locally reconfigure the …

The accumulation of massive amounts of materials data motivates data-based machine learning (ML) approaches. However, an extensive database of materials relying on high-throughput density functional theory (DFT) can be unreliable for emergent properties. Much needed is an approach that can articulate and build on expert human researchers' insights. The tolerance factor introduced in Refs [1-2] articulates a chemical insight for identifying topological semimetals among square-net materials and presents an opportunity to develop such a human-machine synergy. Hence, we developed a supervised-unsupervised hybrid approach combining non-linear Gaussian Process (GP) regression [3] with supervised metric learning to discover descriptors for topological semimetals. Simultaneously, we curated a database containing 1279 square-net materials featuring different physical and chemical attributes and the …

Variational approaches are among the most powerful techniques to approximately solve quantum many-body problems. These encompass both variational states based on tensor or neural networks, and parameterized quantum circuits in variational quantum eigensolvers. However, self-consistent evaluation of the quality of variational wavefunctions is a notoriously hard task. Using a recently developed Hamiltonian reconstruction method, we propose a multi-faceted approach to evaluating the quality of neural-network based wavefunctions. Specifically, we consider convolutional neural network (CNN) and restricted Boltzmann machine (RBM) states trained on a square lattice spin-1/2 J1-J2 Heisenberg model. We find that the reconstructed Hamiltonians are typically less frustrated, and have easy-axis anisotropy near the high frustration point. In addition, the reconstructed Hamiltonians suppress quantum fluctuations in the large J2 limit. Our results highlight the critical importance of the wavefunction's symmetry. Moreover, the multi-faceted insight from the Hamiltonian reconstruction reveals that a variational wave function can fail to capture the true ground state through suppression of quantum fluctuations.

Moiré superlattices comprised of stacked two-dimensional materials present a versatile platform for engineering and investigating new emergent quantum states of matter. At present, the vast majority of investigated systems have long moiré wavelengths, but investigating these effects at shorter, incommensurate wavelengths remains a challenge. How can the electronic features of the superlattice be described when the continuum limit is no longer applicable? Do minibands still form in such short wavelength systems? We demonstrate that a relatively simple tight-binding model treating the effect of the moiré superlattice as an imposed, external potential can accurately describe the electronic structure of new short-wavelength minibands, and that our findings are supported by ab initio calculations. We present our results in tandem with angle-resolved photoemission spectroscopy (ARPES) measurements studying a …

TMD moire systems form a platform for rich interplays between geometric frustration, strong correlation, and quantum fluctuation. Recent experiments [1] at half band filling presented a tantalizing observation of continuous metal-insulator transition (MIT) driven by quantum fluctuation instead of disorder. Motivated by these experiments, we have recently carried out DMRG calculations at 1/2 filling [2] and found a chiral spin liquid phase accompanies the MIT. Here, we turn to fractional filling away from half-filling, where one expects classical charge-ordered states driven by strong further-range interactions [3]. Specifically, we study the quantum melting of the charge-ordered states at 1/3 fillings under the interplay of the strong correlations and the quantum fluctuations using DMRG.

Moir\'e materials such as magic angle twisted bilayer graphene (MATBG) exhibit remarkable phenomenology, but present significant challenges for certain experimental methods, particularly scanning probes such as scanning tunneling microscopy (STM). Typical STM studies that can image tens of thousands of atomic unit cells can image roughly ten moir\'e cells, making data analysis statistically fraught. Here, we propose a method to mitigate this problem by aggregating STM conductance data from several bias voltages, and then using the unsupervised machine learning method of gaussian mixture model clustering to draw maximal insight from the resulting dataset. We apply this method, using as input coarse-grained bond variables respecting the point group symmetry, to investigate nematic ordering tendencies in MATBG for both charge neutral and hole-doped samples. For the charge-neutral dataset, the clustering reveals the surprising coexistence of multiple types of nematicity that are unrelated by symmetry, and therefore generically nondegenerate. By contrast, the clustering in the hole doped data is consistent with long range order of a single type. Beyond its value in analyzing nematicity in MATBG, our method has the potential to enhance understanding of symmetry breaking and its spatial variation in a variety of moir\'e materials.

Making progress in studying the interplay between disorder and fluctuations is challenged by the complexity of extracting robust insights from experiments affected by noise and finite resolution. This has hindered observations of the Bragg glass phase, which is predicted to occur in vortex lattices and charge density wave systems in the presence of disorder. Despite its sharp theoretical definition in terms of diverging correlation lengths, establishing the existence of the Bragg glass phase in a charge density wave system has been challenging. Here, we present the first bulk probe evidence of a Bragg glass phase in the systematically disordered CDW material PdErTe using comprehensive x-ray data and a novel machine learning data analysis tool, X-ray Temperature Clustering (X-TEC). Using data from 20,000 Brillouin zones readily analyzed using X-TEC, we establish a diverging correlation length in samples with moderate intercalation over a wide temperature range. To enable such comprehensive analysis, we introduced a new high-throughput measure of inverse correlation length: "peak-spread". The detection of Bragg glass order and the resulting phase diagram significantly advance our understanding of the complex interplay between disorder and fluctuations. Moreover, using X-TEC to target fluctuations through a high-throughput measure of "peak spread" can revolutionize how the fluctuations are studied in scattering experiments.

Moiré superlattice systems such as transition metal dichalcogenide heterobilayers have garnered significant recent interest due to their promising utility as tunable solid state simulators. Recent experiments on a WSe2/WS2 heterobilayer detected incompressible charge ordered states that one can view as generalized Wigner crystals. The tunability of the transition metal dichalcogenide heterobilayer Moiré system presents an opportunity to study the rich set of possible phases upon melting these charge-ordered states. Here we use Monte Carlo simulations to study these intermediate phases in between incompressible charge-ordered states in the strong coupling limit. We find two distinct stripe solid states to be each preceded by distinct types of nematic states. In particular, we discover microscopic mechanisms that stabilize each of the nematic states, whose order parameter transforms as the two-dimensional E …

With the rapid advancement of quantum computing devices, characterization and validation of many-body quantum states realized on such devices remains an essential challenge. While the full tomographic reconstruction of the density matrix would offer complete characterization of a quantum state, such reconstruction is prohibitively costly for systems larger than a few qubits. Alternatives to tomographic reconstruction are estimating operator expectation values using classical shadows [1] and generative modeling using neural networks such as attention based quantum state tomography (AQT)[2, 3]. We propose to combine the best of both approaches by using AQT-augmented data for classical shadow: Machine-learning Augmented Shadow Tomography (MAST). In this first talk, we present the classical shadow element and the AQT element of the MAST. We also discuss merits of various metrics and subtleties in …

Vestigial nematic and Bragg glass are new phases that emerge when an incommensurate long-range ordered charge density wave (CDW) gets suppressed by a quenched disorder. Pd intercalated rare earth tritelluride (PdxRTe3) has emerged as a promising model system to systematically investigate these emergent phases when the bi-directional orthogonal CDW order of RTe3 interacts with a controlled amount of disorder (Pd intercalation). Using X-ray diffraction Temperature Clustering (X-TEC), an unsupervised and interpretable machine learning technique introduced in Ref.[1], we extract signatures of Bragg glass and vestigial nematic phases in PdxErTe3 from a large volume of X-ray temperature series data spanning 20000 Brillouin zones, collected using the Pilatus 2M CdTe detector on Sector 6-ID-D at the Advanced Photon Source. In addition to identifying the two order parameters from the CDW peak …

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern X-ray facilities have allowed a dramatically increased fraction of this information to be captured. Now, the primary challenge is to understand and discover scientific principles from big datasets when a comprehensive analysis is beyond human reach. We report the development of an unsupervised machine learning approach, X-ray diffraction (XRD) temperature clustering (X-TEC), that can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from a series of high-volume X-ray diffraction measurements taken at multiple temperatures. We benchmark X-TEC with diffraction data on a quasi-skutterudite family of materials, (CaxSr …

The observation of superconductivity and correlated insulating states in twisted bilayer graphene has motivated much theoretical progress at integer fillings. However, little attention has been given to fractional fillings. Here we show that the three-peak structure of Wannier orbitals, dictated by the symmetry and topology of flat bands, facilitates the emergence of a state we name a “fractional correlated insulator” at commensurate fractional filling of ν = n ± 1/3. Specifically for the filling of 1/3 electrons per moiré unit cell, we show that short-range interactions lead to an extensive entropy due to the “breathing” degree of freedom of an irregular honeycomb lattice that emerges through defect lines. The leading further-range interaction lifts this degeneracy and selects a ferromagnetic nematic state that breaks AB/BA sublattice symmetry. The proposed fractional correlated insulating state might underlie the suppression of …

The challenge brought to scientific discovery by the data revolution may be overcome by data scientific approaches. Here we focus on 4D scanning transmission electron microscopy (STEM) data. With advances in detector technology, STEM records the full scattering distribution at each scan position in real space, producing a 4D phase-space distribution. An efficient approach is needed to turn these data into a real space image with subatomic resolution. Existing approaches are limited: annular dark field (ADF) imaging by low dose efficiency and resolution, and ptychography by high computational cost. Here, we develop an efficient, interpretable machine learning model to map the entire STEM dataset to real-space images. Our model is able to find an intra-unit cell distortion in a sample of PrScO 3 that is missed by ADF using data that cannot be used for ptychography.

As a unique opportunity hosted by the magic angle twisted bilayer graphene (MATBG), the emergence of a novel fractional correlated insulating (FCI) state dictated by the geometry of the three-peak structure of Wannier orbitals has been proposed [1]. While Ref.[1] established the novel FCI state with extensive ground-state landscape at the filling of n+/-1/3 from rigorous considerations in the strong coupling limit, little is known about the nature of excitations. At the same time, novel correlated ground states often host unusual excitations. In this talk, I will discuss the nature of two distinct fractionalized excitations in the FCI phase.:(1) fracton-like vortices and (2) soliton-like domain wall edge states. Although both types of excitations carry fractional electric charges, their topological charges are distinct. Moreover, both defects' movements are restricted to emergent low-dimensional subspace embedded in the two …

Conventional gating in transistors uses electric fields through external dielectrics that require complex fabrication processes. Various optoelectronic devices deploy photogating by electric fields from trapped charges in neighbor nanoparticles or dielectrics under light illumination. Orbital gating driven by giant Stark effect is demonstrated in tunneling phototransistors based on 2H‐MoTe2 without using external gating bias or slow charge trapping dynamics in photogating. The original self‐gating by light illumination modulates the interlayer potential gradient by switching on and off the giant Stark effect where the dz2‐orbitals of molybdenum atoms play the dominant role. The orbital gating shifts the electronic bands of the top atomic layer of the MoTe2 by up to 100 meV, which is equivalent to modulation of a carrier density of 7.3 × 1011 cm–2 by electrical gating. Suppressing conventional photoconductivity, the orbital …

Moiré superlattices constructed from transition metal dichalcogenides have demonstrated a series of emergent phenomena, including moiré excitons, flat bands, and correlated insulating states. All of these phenomena depend crucially on the presence of strong moiré potentials, yet the properties of these moiré potentials, and the mechanisms by which they can be generated, remain largely open questions. Here, we use angle-resolved photoemission spectroscopy with submicron spatial resolution to investigate an aligned WS2/WSe2 moiré superlattice and graphene/WS2/WSe2 trilayer heterostructure. Our experiments reveal that the hybridization between moiré bands in WS2/WSe2 exhibits an unusually large momentum dependence, with the splitting between moiré bands at the Γ point more than an order of magnitude larger than that at K point. In addition, we discover that the same WS2/WSe2 superlattice can …

Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for optimal interface design. Here we introduce a high-throughput computational framework, InterMatch, for efficiently predicting charge transfer, strain, and superlattice structure of an interface by leveraging the databases of individual bulk materials. Specifically, the algorithm reads in the lattice vectors, density of states, and the stiffness tensors for each material in their isolated form from the Materials Project. From these bulk properties, InterMatch estimates the interfacial properties. We benchmark InterMatch predictions for the charge transfer against experimental measurements and supercell density-functional theory calculations. We then use InterMatch to predict promising interface candidates for doping transition metal dichalcogenide MoSe. Finally, we explain experimental observation of factor of 10 variation in the supercell periodicity within a few microns in graphene/-RuCl by exploring low energy superlattice structures as a function of twist angle using InterMatch. We anticipate our open-source InterMatch algorithm accelerating and guiding ever-growing interfacial design efforts. Moreover, the interface database resulting from the InterMatch searches presented in this paper can be readily accessed through https://contribs.materialsproject.org/projects/intermatch/ .

Evaluating the quality of variational wavefunctions is a hard task due to the large dimensionality of Hilbert space. At the same time, modern methods such as artificial neural networks or variational quantum eigensolvers need accurate evaluation of wavefunctions to facilitate effective development. We propose using a recently developed Hamiltonian reconstruction method for a multi-faceted approach to evaluating wavefunctions. Starting from convolutional neural network and restricted Boltzmann machine ansatze trained on a square lattice spin-1/2 J 1-J 2 Heisenberg model, we compare reconstructed Hamiltonians to the original Hamiltonian to evaluate various aspects of the wavefunction. The reconstructed Hamiltonians are systematically 1) less frustrated, and 2) have easy-axis anisotropy near the high frustration point. Also, in the large J 2 limit, further-range interactions are induced in the reconstructions. This …

In recent years, the notion of'Quantum Materials' has emerged as a powerful unifying concept across diverse fields of science and engineering, from condensed-matter and coldatom physics to materials science and quantum computing. Beyond traditional quantum materials such as unconventional superconductors, heavy fermions, and multiferroics, the field has significantly expanded to encompass topological quantum matter, two-dimensional materials and their van der Waals heterostructures, Moiré materials, Floquet time crystals, as well as materials and devices for quantum computation with Majorana fermions. In this Roadmap collection we aim to capture a snapshot of the most recent developments in the field, and to identify outstanding challenges and emerging opportunities. The format of the Roadmap, whereby experts in each discipline share their viewpoint and articulate their vision for quantum materials …

Recent observations of nonlocal transport in ultraclean two-dimensional materials raised the tantalizing possibility of accessing hydrodynamic correlated transport of a many-electron state. However, it has been pointed out that nonlocal transport can also arise from impurity scattering rather than interaction. At the crux of the ambiguity is the focus on linear effects, ie, Ohm's law, which cannot easily differentiate among different modes of transport. Here we propose experiments that can reveal rich hydrodynamic features in the system by tapping into the nonlinearity of the Navier-Stokes equation. Three experiments we propose will each manifest a unique phenomenon that is well known in classical fluids: the Bernoulli effect, Eckart streaming, and Rayleigh streaming. Analysis of known parameters confirms that the proposed experiments are feasible and the hydrodynamic signatures are within reach of graphene-based …

Even as the understanding of the mechanism behind correlated insulating states in magic-angle twisted bilayer graphene converges toward various kinds of spontaneous symmetry breaking, the metallic “normal state” above the insulating transition temperature remains mysterious, with its excessively high entropy and linear-in-temperature resistivity. In this Letter, we focus on the effects of fluctuations of the order parameters describing correlated insulating states at integer fillings of the low-energy flat bands on charge transport. Motivated by the observation of heterogeneity in the order-parameter landscape at zero magnetic field in certain samples, we conjecture the existence of frustrating extended-range interactions in an effective Ising model of the order parameters on a triangular lattice. The competition between short-distance ferromagnetic interactions and frustrating extended-range antiferromagnetic …

Spin-orbit coupling in transition metal compounds with 4d and 5d electrons is predicted to generate a wide variety of novel parity-breaking correlated electron phases, but evidence of a concomitant structural response is often lacking. Cd 2 Re 2 O 7 is a pyrochlore that has been proposed to exhibit multipolar nematic order below two structural phase transitions at 200K and 113K, but the symmetry of the order parameters are under dispute. We report high-energy x-ray scattering measurements of 3D reciprocal space volumes comprising over 10,000 Brillouin zones in a fine Q-grid performed at many temperatures from 300K to 30K. We have analyzed the data with unsupervised machine learning, using the newly-developed X-TEC algorithm, to classify the temperature dependence of both superlattice peaks and diffuse scattering. The order parameter below 200K results from cation displacements consistent with the …

Moire systems provide a rich platform for studies of strong correlation physics. A recent experiment with transition metal dichalcogenide (TMD) bilayer realized an experimental simulation of the extended Hubbard model on a triangular lattice. Inspired by this experiment, we explore the charge order phenomena of the extended Hubbard model on the triangular lattice using the density matrix renormalization group (DMRG).

Despite their fundamental importance in dictating the quantum-mechanical properties of a system, ground states of many-body local quantum Hamiltonians form a set of measure zero in the many-body Hilbert space. Hence determining whether a given many-body quantum state is ground-stateable is a challenging task. Here we propose an unsupervised machine learning approach, dubbed Entanglement Clustering (“EntanCl”), to separate out ground-stateable wave functions from those that must be excited-state wave functions using entanglement structure information. EntanCl uses snapshots of an ensemble of swap operators as input and projects these high-dimensional data to two dimensions, preserving important topological features of the data associated with distinct entanglement structure using the uniform manifold approximation and projection. The projected data are then clustered using K-means …

With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the'attention-based quantum tomography'(AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in'Attention is all you need'by Vaswani et al (2017 NIPS) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing (NLP) models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that …

Recent experiments report ubiquitous observations of correlated insulating states at integer fillings of low energy bands, that give way to strange metal transport with T-linear resistivity upon heating, in magic-angle twisted bilayer graphene (MATBG). However, a theoretical framework that provides an understanding of this strange metal transport starting from the low energy correlated insulator physics of MATBG remains lacking. In this work, we focus on local tendencies for spontaneous symmetry breaking in the order parameters describing topological correlated insulating states. We consider the effect of the redirection of bulk current flow due to fluctuations of the order parameter landscape on transport. We find that thermal fluctuations in the landscape of heterogeneous order parameter configurations above the ordering transition lead to T-linear resistivity over a wide range of temperatures. Furthermore, we …

Recent developments in 2D incommensurate atomic heterostructures reveal a vast phase space of complex systems, rich in exotic phenomena and opportunities for control. Among these systems are hetero-bilayers, which can offer new opportunities to create designed interfacial systems. Nevertheless, the non-crystalline nature of such systems challengesab initio predictions for the interfaces. I will present a new theoretical ab initio framework for mismatched hetero-bilayers, Mismatched INterface Theory (MINT), and its application to α-RuCl3 heterostructures. Furthermore, I will discuss our efforts to integrate MINT and other continuum theories that enable accurate modeling of charge transfer, strain, spin-orbit interactions, and magnetism of incommensurate interfaces with the open-access materialsproject. org. The integration will result in a versatile interface database tool that predicts charge transfer, strain, and …

MachineLearning Quantum Emergence Page 1 MachineLearning Quantum Emergence Eun-Ah Kim (Cornell) KITP, Feb14, 2019 Page 2 Learning Quantum Emergence Simple Principles Complex Reality of Data Entropy Phonons Topological Invariant Fermi statistics Phase Diagrams Many-body WF Quantum Complexity Page 3 Data Driven Challenges Page 4 Complexity of Quantum Many-body State Many body wave function of 49 electrons: a function in trillion trillion trillion trillion dimensional space Page 5 Tunneling Density of States, in 1962 Giaever et al, Phys. Rev. 126, 941 (1962) Differential conductance dI/dV @ V proportional to N(E=eV) Page 6 Generative Model Schrieffer, Scalapino, Wilkins, PRL 10, 336 (1963) Page 7 Imaging N(r,E): Scanning Tunneling Spectroscopy Tunneling Density of States, in 2000’s Page 8 Data-driven challenges in Reciprocal Space Diffuse Scattering data on TiSe2 at 150 K (…

Decades of efforts in improving instrumentation and sensors led us to the age of voluminous scattering data. However, the radically increased volume and experimental control present new challenges. I will discuss how these challenges can be embraced and turned into opportunities by employing machine learning. The rigorous framework for scientific understanding physicists enjoy through our celebrated tradition requires the interpretability of any machine learning essential. I will discuss our recent results using machine learning approaches designed to be interpretable from the outset. Specifically, I will present discovering order parameters and its fluctuations in voluminous X-ray diffraction data using a X-ray TEmperatures Clustering (X-TEC) we introduced in Ref [1].

Recent developments in 2D incommensurate atomic heterostructures reveal a vast phase space of complex systems rich in exotic phenomena and opportunities for control. These developments include cutting-edge computational tools such as Mismatched Interface Theory (MINT) and other continuum theories that enable accurate modeling of charge transfer, strain, spin-orbit interactions, and magnetism of incommensurate interfaces that were previously inaccessible to traditional ab initiotechniques. We combine these advances with the open access materialsproject. org to develop a versatile interface database tool that predicts charge transfer, strain, and other crucial parameters of an interface between two arbitrary materials.

Nematic order can arise from a number of physical origins. A vestigial nematic order associated with a disordered uni-directional charge density wave (CDW) has been a topic of much theoretical interest with the relatively little direct experimental investigation. Here, we use diffuse x-ray scattering to study the effects of Pd-intercalation, which introduces controlled disorder, on CDW formation in ErTe3, a weakly orthorhombic material for which CDW fluctuations are present in both in-plane directions. For this, three-dimensional reciprocal space volumes were collected covering 20000 Brillouin Zones using the Pilatus 2M CdTe detector on Sector 6-ID-D at the Advanced Photon Source. We then use our recently developed machine learning tool, X-TEC to explore these comprehensive data sets. For the pristine compound, the tool is quickly able to identify the onset of two CDW transitions and the associated order …

Bandgap engineering is central to the design of heterojunction devices. For heterojunctions involving monolayer-thick materials like MoS 2, the carrier concentration of the atomically thin film can vary significantly depending on the amount of charge transfer between MoS 2 and the substrate. This makes substrates with a range of charge neutrality levels—as is the case for complex oxide substrates—a powerful addition to electrostatic gating or chemical doping to control the doping of overlying MoS 2 layers. We demonstrate this approach by growing monolayer MoS 2 on perovskite (SrTiO 3 and LaAlO 3), spinel (MgAl 2 O 4), and SiO 2 substrates with multi-inch uniformity. The as-grown MoS 2 films on these substrates exhibit a controlled, reproducible, and uniform carrier concentration ranging from (1–4)× 10 13 cm− 2, depending on the oxide substrate employed. The observed carrier concentrations are further …

Non-Fermi liquid physics is ubiquitous in strongly correlated metals, manifesting itself in anomalous transport properties, such as a T-linear resistivity in experiments. However, its theoretical understanding in terms of microscopic models is lacking, despite decades of conceptual work and attempted numerical simulations. Here we demonstrate that a combination of sign-problem-free quantum Monte Carlo sampling and quantum loop topography, a physics-inspired machine-learning approach, can map out the emergence of non-Fermi liquid physics in the vicinity of a quantum critical point (QCP) with little prior knowledge. Using only three parameter points for training the underlying neural network, we are able to robustly identify a stable non-Fermi liquid regime tracing the fans of metallic QCPs at the onset of both spin-density wave and nematic order. In particular, we establish for the first time that a spin-density wave …

Non-Fermi liquid physics is a ubiquitous feature in strongly correlated metals, manifesting itself in anomalous transport properties, such as a T-linear resistivity in experiments. However, its theoretical understanding in terms of microscopic models is lacking despite decades of conceptual work and numerical simulations. Here we demonstrate that a combination of sign problem-free quantum Monte Carlo sampling and quantum loop topography, a physics-inspired machine learning approach, can map out the emergence of non-Fermi liquid physics in the vicinity of a quantum critical point with little prior knowledge. Using only three parameter points for training the underlying neural network, we are able to reproducibly identify a stable non-Fermi liquid regime tracing the fans of metallic quantum critical points at the onset of both spin-density wave and nematic order. Our study thereby provides an important proof-of …

Moiré systems have recently garnered tremendous attention due to their highly tunable electronic properties, which allow them to realize a rich set of electronic states. Recent experiments on a WS2/WSe2 hetero-bilayer system reveal a series of charge-ordered states at various commensurate Moiré lattice fillings. These experiments also find evidence for rotational symmetry breaking in the entire filling region between 1/3 and 2/3. Specifically, we use Monte Carlo simulations to explore the phase diagram for charge density wave and nematic phases at non-zero temperatures in the vicinity of and away from various commensurate densities. We also study the properties of topological defects involved in the melting of the charge density waves.

FeSeTe has recently emerged as a leading candidate material for the two-dimensional topological superconductivity (TSC). Two reasons for the excitement are the high T c of the system and the fact that the Majorana zero modes (MZMs) inside the vortex cores live on the exposed surface rather than at the interface of a heterostructure as in the proximitized topological insulators. However, the recent scanning tunneling spectroscopy data have shown that, contrary to the theoretical expectation, the MZM does not exist inside every vortex core. Hence there are “full” vortices with MZMs and “empty” vortices without MZMs. Moreover, the fraction of “empty” vortices increases with an increase in the magnetic field. We propose the possibility of two distinct gapped states competing for the topological surface states in FeSeTe: the TSC and half quantum anomalous Hall (hQAH). The latter is promoted by a magnetic field …

Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from such approaches. Here, we develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of physical observables. Applied to simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard …

In many unconventional superconductors, nematic quantum fluctuations are strongest where the critical temperature is highest, inviting the conjecture that nematicity plays an important role in the pairing mechanism. Recently, Ba 1− x Sr x Ni 2 As 2 has been identified as a tunable nematic system that provides an ideal testing ground for this proposition. We therefore propose several sharp empirical tests, supported by quantitative calculations in a simple model of Ba 1− x Sr x Ni 2 As 2. The most stringent predictions concern experiments under uniaxial strain, which has recently emerged as a powerful tuning parameter in the study of correlated materials. Since uniaxial strain so precisely targets nematic fluctuations, such experiments may provide compelling evidence for nematic-mediated pairing, analogous to the isotope effect in conventional superconductors.

U39. 00011: Unsupervised machine learning for accelerating discoveries from temperature dependent X-ray data*

Two-dimensional nanoelectronics, plasmonics, and emergent phases require clean and local charge control, calling for layered, crystalline acceptors or donors. Our Raman, photovoltage, and electrical conductance measurements combined with ab initio calculations establish the large work function and narrow bands of α-RuCl3 enable modulation doping of exfoliated single and bilayer graphene, chemical vapor deposition grown graphene and WSe2, and molecular beam epitaxy grown EuS. We further demonstrate proof of principle photovoltage devices, control via twist angle, and charge transfer through hexagonal boron nitride. Short-ranged lateral doping (≤65 nm) and high homogeneity are achieved in proximate materials with a single layer of α-RuCl3. This leads to the best-reported monolayer graphene mobilities (4900 cm2/(V s)) at these high hole densities (3 × 1013 cm–2) and yields larger charge …

Recent experimental observations of apparently hydrodynamic electronic transport have generated much excitement. However, the understanding of the observed nonlocal transport (whirlpool) effects and parabolic (Poiseuille-like) current profiles has largely been motivated by a phenomenological analogy to classical fluids. This is due to difficulty in incorporating strong correlations in quantum mechanical calculation of transport, which has been the primary angle for interpreting the apparently hydrodynamic transport. Here we demonstrate that even free-fermion systems, in the presence of (inevitable) disorder, exhibit nonlocal conductivity effects such as those observed in experiments because of the fermionic system's long-range entangled nature. On the basis of explicit calculations of the conductivity at finite wave vector, σ (q), for selected weakly disordered free-fermion systems, we propose experimental …

In many unconventional superconductors, nematic quantum fluctuations are strongest where the critical temperature is highest, inviting the conjecture that nematicity plays an important role in the pairing mechanism. Recently, strontium-doped barium nickel oxide has been identified as a tunable nematic system that provides an ideal testing ground for this proposition. We therefore propose several sharp empirical tests, supported by quantitative calculations in a simple model of this material. The most stringent predictions concern experiments under uniaxial strain, which has recently emerged as a powerful tuning parameter in the study of correlated materials. Since uniaxial strain so precisely targets nematic fluctuations, such experiments may provide compelling evidence for nematic-mediated pairing in this and other materials, analogous to the isotope effect in conventional superconductors.* SL was supported in …

A theoretical understanding of the enigmatic linear-in-temperature (T) resistivity, ubiquitous in strongly correlated metallic systems, has been a long sought-after goal. Furthermore, the slope of this robust T-linear resistivity is also observed to stay constant through crossovers between different temperature regimes: a phenomenon we dub “slope invariance.” Recently, several solvable models with T-linear resistivity have been proposed, putting us in an opportune moment to compare their inner workings in various explicit calculations. We consider two strongly correlated models with local self-energies that demonstrate T linearity: a lattice of coupled Sachdev-Ye-Kitaev models and the Hubbard model in single-site dynamical mean-field theory. We find that the two models achieve T linearity through distinct mechanisms at intermediate temperatures. However, we also find that these mechanisms converge to an …

Recent developments in twisted and lattice-mismatched bilayers have revealed a rich phase space of van der Waals systems and generated excitement. Among these systems are heterobilayers, which can offer new opportunities to control van der Waals systems with strong in plane correlations such as spin-orbit-assisted Mott insulator α− RuCl 3. Nevertheless, a theoretical ab initio framework for mismatched heterobilayers without even approximate periodicity is sorely lacking. We propose a general strategy for calculating electronic properties of such systems, mismatched interface theory (MINT), and apply it to the graphene/α− RuCl 3 (GR/α− RuCl 3) heterostructure. Using MINT, we predict uniform doping of 4.77% from graphene to α− RuCl 3 and magnetic interactions in α− RuCl 3 to shift the system toward the Kitaev point. Hence, we demonstrate that MINT can guide targeted materialization of desired model …

Anomalous transport, such as T-linear resistivity (a hallmark of non-Fermi liquid behavior), is a ubiquitous feature of strongly correlated metallic systems, but famously difficult to understand theoretically. For relevant simple models, the transport computations of even numerically exact Monte Carlo simulations are subject to enormous systematic errors and come at great additional computational cost. Building on earlier work, we apply quantum loop topography (QLT) and supervised learning on quantum Monte Carlo data to examine the Fermi liquid to non-Fermi liquid crossover in models of both Ising nematic and spin density wave quantum criticality. Previous work on these models has demonstrated this crossover using measurements of correlation functions at nonzero imaginary time separation. Our results, using only equal time measurements, show good agreement with these previous results at dramatically …

“Strange metals” with resistivity depending linearly on temperature down to low have been a long-standing puzzle in condensed matter physics. Here, we consider a lattice model of itinerant spin- fermions interacting via onsite Hubbard interaction and random infinite-ranged spin–spin interaction. We show that the quantum critical point associated with the melting of the spin-glass phase by charge fluctuations displays non-Fermi liquid behavior, with local spin dynamics identical to that of the Sachdev-Ye-Kitaev family of models. This extends the quantum spin liquid dynamics previously established in the large- limit of symmetric models to models with physical spin- electrons. Remarkably, the quantum critical regime also features a Planckian linear- resistivity associated with a -linear scattering rate and a frequency dependence of the electronic self-energy consistent with the marginal …

W63. 00010: Unsupervised learning of nematic order from scanning tunneling spectroscopy on twisted bilayer graphene*AbstractPresenter:Samuel Lederer(Cornell University)Authors:Samuel Lederer(Cornell University)Youngjoon Choi(Caltech)Pavel Ismailov(Cornell University)Andrew Wilson(NYU)Stevan Nadj-Perge(Caltech)Eun-Ah Kim(Cornell University)Moiré materials such as magic angle twisted bilayer graphene (TBG) provide an exciting platform for the study of novel states of matter, but their large unit cells present significant difficulties for atomic resolution probes such as scanning tunneling microscopy (STM). We therefore develop an automated method to extract salient physical quantities from STM data on moiré materials, and apply it to measurements on TBG that visually suggest the breaking of rotational symmetry (ie nematic order). We apply the machine learning technique of Gaussian mixture …

Recently we we proposed a new approach to engineering exotic superconductors based on a metal/quantum spin ice heterostructure. However, pyrochlore materials are difficult to grow and current models of their spin fluctuation spectra remain incomplete. In contrast, weakly-interacting spin dimer compounds such as Ba3Mn2O8 are experimentally tractable and their spin correlations can be calculated explicitly. In this work we focus on a new example of such a setup, the spin dimer compound Ba3Mn2O8 in heterostructure with electron-doped Ba3Sb2O8, and predict interfacial p+ ip pairing at a few Kelvin. We also provide criteria for materializing the heterostructure using ab initio calculations. Hence we present a concrete proposal based on a controlled calculation predicting p+ ip superconductivity in a metal/quantum dimer heterostructure.

Modern direct electron detectors enable recording the full scattering distribution from an electron beam focused to atomic dimensions at imaging speeds, producing 4-dimensional, phase-space data sets. Current data rates are Gb/s, with Tb/s expected shortly as detector speeds increase, following a Moore’s-law-like scaling as the underlying silicon technology improves [1, 2]. Encoded in these large and growing data sets are both the phase and amplitude of the exit wave, from which the expectation values of physical operators can be reconstructed to learn the structure and fields in the probed sample. However, real space images of atomic structure need to be reconstructed from the scattering data. Applying a linear filter such as an annular dark field (ADF) mask quickly generates an interpretable image at the cost of discarding a large fraction of the data. On the other hand, ptychography takes full advantage of the …

The problem of many-body wavefunction reconstruction, which suffers from exponential scaling in system size as well as noisy state preparation and measurement, remains a major obstacle to the study of intermediate-scale quantum systems. Recent works found success by recasting the problem of reconstruction to learning the probability distribution of quantum state measurement vectors, a natural task for generative neural network models. Networks based on the attention mechanism, designed to learn long-range correlations in natural language sentences, appear especially well-suited to the task of learning highly entangled wavefunctions. In this work, we demonstrate that an attention mechanism-based generative network, based on the model proposed in``Attention is all you need’’by Vishwani et al (2017), can outperform previous neural network based approaches to quantum state tomography. Specifically …

There has been growing excitement over the possibility of employing artificial neural networks (ANNs) to gain new theoretical insight into the physics of quantum many-body problems.“Interpretability” remains a concern: can we understand the basis for the ANN's decision-making criteria in order to inform our theoretical understanding?“Interpretable” machine learning in quantum matter has to date been restricted to linear models, such as support vector machines, due to the greater difficulty of interpreting nonlinear ANNs. Here we consider topological quantum phase transitions in models of Chern insulator, Z 2 topological insulator, and Z 2 quantum spin liquid, each using a shallow fully connected feed-forward ANN. The use of quantum loop topography, a “domain knowledge”–guided approach to feature selection, facilitates the construction of faithful phase diagrams and semiquantitative interpretation of the criteria …

The unusual correlated state that emerges in URu2Si2 below THO = 17.5 K is known as “hidden order” because even basic characteristics of the order parameter, such as its dimensionality (whether it has one component or two), are “hidden.” We use resonant ultrasound spectroscopy to measure the symmetry-resolved elastic anomalies across THO. We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a …