arXiv subjects

Mingda Li

Publications and source records attributed to Mingda Li.

At least 91 records · Page 5Linked to original sources

arXiv2022

Testing the data framework for an AI algorithm in preparation for high data rate X-ray facilities

The advent of next-generation X-ray free electron lasers will be capable of delivering X-rays at a repetition rate approaching 1 MHz continuously. This will require the development of data systems to handle experiments at these type of facilities, especially for high throughput applications, such as femtosecond X-ray crystallography and X-ray photon fluctuation spectroscopy. Here, we demonstrate a framework which captures single shot X-ray data at the LCLS and implements a machine-learning algorithm to automatically extract the contrast parameter from the collected data. We measure the time required to return the results and assess the feasibility of using this framework at high data volume. We use this experiment to determine the feasibility of solutions for `live' data analysis at the MHz repetition rate.

physics.data-an↗

arXiv2022

Machine learning spectral indicators of topology

Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands of candidate topological materials, experimental determination of materials' topology often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique sensitive to atoms' local symmetry and chemical bonding, which are intimately linked to band topology by the theory of topological quantum chemistry (TQC). Moreover, as a local structural probe, XAS is known to have high quantitative agreement between experiment and calculation, suggesting that insights from computational spectra can effectively inform experiments. In this work, we leverage computed X-ray absorption near-edge structure (XANES) spectra of more than 10,000 inorganic materials to train a neural network (NN) classifier that predicts topological class directly from XANES signatures, achieving F$_1$ scores of 89% and 93% for topological and trivial classes, respectively. Additionally, we obtain consistent classifications using corresponding experimental and computational XANES spectra for a small number of measured compounds. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine learning-augmented XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials, and may further inform field-driven phenomena in situ, such as magnetic field-driven topological phase transitions.

cond-mat.dis-nn↗

arXiv2022

SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation

This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire dialogue quality. We first propose a novel automatic data construction method that can automatically assign fine-grained scores for arbitrarily dialogue data. Then we train \textbf{SelF-Eval} with a multi-level contrastive learning schema which helps to distinguish different score levels. Experimental results on multiple benchmarks show that SelF-Eval is highly consistent with human evaluations and better than the state-of-the-art models. We give a detailed analysis of the experiments in this paper. Our code is available on GitHub.

cs.CL↗

arXiv2022

Demonstration of LogicLib: An Expressive Multi-Language Interface over Scalable Datalog System

With the ever-increasing volume of data, there is an urgent need to provide expressive and efficient tools to support Big Data analytics. The declarative logical language Datalog has proven very effective at expressing concisely graph, machine learning, and knowledge discovery applications via recursive queries. In this demonstration, we develop Logic Library (LLib), a library of recursive algorithms written in Datalog that can be executed in BigDatalog, a Datalog engine on top of Apache Spark developed by us. LLib encapsulates complex logic-based algorithms into high-level APIs, which simplify the development and provide a unified interface akin to the one of Spark MLlib. As LLib is fully compatible with DataFrame, it enables the integrated utilization of its built-in applications and new Datalog queries with existing Spark functions, such as those provided by MLlib and Spark SQL. With a variety of examples, we will (i) show how to write programs with LLib to express a variety of applications; (ii) illustrate its user experience in Apache Spark ecosystem; and (iii) present a user-friendly interface to interact with the LLib framework and monitor the query results.

cs.DB↗

arXiv2022

Recent Progress in Conversational AI

Conversational artificial intelligence (AI) is becoming an increasingly popular topic among industry and academia. With the fast development of neural network-based models, a lot of neural-based conversational AI system are developed. We will provide a brief review of the recent progress in the Conversational AI, including the commonly adopted techniques, notable works, famous competitions from academia and industry and widely used datasets.

cs.CL↗

arXiv2022

One Dimensional Wormhole Corrosion in Metals

Corrosion is a ubiquitous failure mode of materials in extreme environments. The more localized it is, the more difficult it is to detect and more deleterious its effects. Often, the progression of localized corrosion is accompanied by the evolution of porosity in materials, creating internal void-structures that facilitate the ingress of the external environment into the interior of the material, further accelerating the internal corrosion. Previously, the dominant morphology of such void-structures has been reported to be either three-dimensional (3D) or two-dimensional (2D). Here, we report a more localized form of corrosion, which we call 1D wormhole corrosion. Using electron tomography, we show multiple examples of this 1D and percolating morphology that manifests a significantly high aspect ratio differentiable from 2D and 3D corrosion. To understand the origin of this mechanism in a Ni-Cr alloy corroded by molten salt, we combined energy-filtered four-dimensional scanning transmission electron microscopy (EF-4D-STEM) and ab initio density functional theory (DFT) calculations to develop a vacancy mapping method with nanometer-resolution, identifying a remarkably high vacancy concentration in the diffusion-induced grain boundary migration (DIGM) zone, up to 100 times the equilibrium value at the melting point. These vacancy supersaturation regions act as the precursors of wormholes, and lead to the asymmetrical growth of voids along GBs. We show that similar 1D penetrating corrosion morphologies could also occur in other materials or corrosion conditions, implying the broad impact of this extremely localized corrosion mechanism. Deciphering the origins of 1D corrosion is an important step towards designing structural materials with enhanced corrosion resistance, and also offers new pathways to create ordered-porous materials for functional applications.

cond-mat.mtrl-sci↗

arXiv2022

Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge to parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator-ferromagnetic insulator heterostructure Bi$_2$Se$_3$/EuS exhibiting proximity magnetism, in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator-antiferromagnet heterostructure (Bi,Sb)$_2$Te$_3$/Cr$_2$O$_3$ and identify possible interfacial proximity magnetism in this material. We anticipate the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.

cond-mat.mtrl-sci↗

arXiv2022

Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning

One central challenge in understanding phonon thermal transport is a lack of experimental tools to investigate mode-based transport information. Although recent advances in computation lead to mode-based information, it is hindered by unknown defects in bulk region and at interfaces. Here we present a framework that can reveal microscopic phonon transport information in heterostructures, integrating state-of-the-art ultrafast electron diffraction (UED) with advanced scientific machine learning. Taking advantage of the dual temporal and reciprocal-space resolution in UED, we are able to reliably recover the frequency-dependent interfacial transmittance with possible extension to frequency-dependent relaxation times of the heterostructure. This enables a direct reconstruction of real-space, real-time, frequency-resolved phonon dynamics across an interface. Our work provides a new pathway to experimentally probe phonon transport mechanisms with unprecedented details.

cond-mat.mtrl-sci↗

arXiv2022

Electronic properties of correlated kagomé metals AV$_3$Sb$_5$(A = K, Rb, Cs): A perspective

Following the discovery of a new family of kagomé prototypical materials with structure AV$_3$Sb$_5$ (A = K, Rb, Cs), there has been heightened interest in studying the correlation-driven electronic phenomena in these kagomé lattice systems. The study of these materials has gone beyond magneto-transport measurements to reveal exciting features such as Dirac bands, anomalous Hall effect, bulk superconductivity with $T_c$ $\sim$ 0.9 K-2.5 K, and the observation of charge density wave instabilities, suggesting an intertwining of topological physics and new quantum orders. Moreover, very recent works on numerous types of experiments have appeared further examining the unconventional superconductivity and the exotic electronic states found within these kagomé materials. Theories on the strong interactions that play a role in these systems have been proposed to shed light on the nature of these topological charge density waves. In this brief review, we summarize these recent experimental findings and theoretical proposals, and envision the materials as new platforms to study the interplay between topological physics and strongly-correlated electronic systems.

cond-mat.str-el↗

arXiv2022

Topological Signatures in Nodal Semimetals through Neutron Scattering

Topological nodal semimetals are known to host a variety of fascinating electronic properties due to the topological protection of the band-touching nodes. Neutron scattering, despite its power in probing elementary excitations, has not been routinely applied to topological semimetals, mainly due to the lack of an explicit connection between the neutron response and the signature of topology. In this work, we theoretically investigate the role that neutron scattering can play to unveil the topological nodal features: a large magnetic neutron response with spectral non-analyticity can be generated solely from the nodal bands. A new formula for the dynamical structure factor for generic topological nodal metals is derived. For Weyl semimetals, we show that the locations of Weyl nodes, the Fermi velocities and the signature of chiral anomaly can all leave hallmark neutron spectral responses. Our work offers a neutron-based avenue towards probing bulk topological materials.

cond-mat.mtrl-sci↗

arXiv2021

Extended Kohler$^,$s Rule of Magnetoresistance

A notable phenomenon in topological semimetals is the violation of Kohler$^,$s rule, which dictates that the magnetoresistance $MR$ obeys a scaling behavior of $MR = f(H/ρ_0$), where $MR = [ρ_H-ρ_0]/ρ_0$ and $H$ is the magnetic field, with $ρ_H$ and $ρ_0$ being the resistivity at $H$ and zero field, respectively. Here we report a violation originating from thermally-induced change in the carrier density. We find that the magnetoresistance of the Weyl semimetal, TaP, follows an extended Kohler$^,$s rule $MR = f[H/(n_Tρ_0)]$, with $n_T$ describing the temperature dependence of the carrier density. We show that $n_T$ is associated with the Fermi level and the dispersion relation of the semimetal, providing a new way to reveal information on the electronic bandstructure. We offer a fundamental understanding of the violation and validity of Kohler$^,$s rule in terms of different temperature-responses of $n_T$. We apply our extended Kohler$^,$s rule to BaFe$_2$(As$_{1-x}$P$_x$)$_2$ to settle a long-standing debate on the scaling behavior of the normal-state magnetoresistance of a superconductor, namely, $MR$ ~ $tan^2θ_H$, where $θ_H$ is the Hall angle. We further validate the extended Kohler$^,$s rule and demonstrate its generality in a semiconductor, InSb, where the temperature-dependent carrier density can be reliably determined both theoretically and experimentally.

cond-mat.mtrl-sci↗

arXiv2021

Signature of Many-Body Localization of Phonons in Strongly Disordered Superlattices

Many-body localization (MBL) has attracted significant attention due to its immunity to thermalization, role in logarithmic entanglement entropy growth, and opportunities to reach exotic quantum orders. However, experimental realization of MBL in solid-state systems has remained challenging. Here we report evidence of a possible phonon MBL phase in disordered GaAs/AlAs superlattices. Through grazing-incidence inelastic X-ray scattering, we observe a strong deviation of the phonon population from equilibrium in samples doped with ErAs nanodots at low temperature, signaling a departure from thermalization. This behavior occurs within finite phonon energy and wavevector windows, suggesting a localization-thermalization crossover. We support our observation by proposing a theoretical model for the effective phonon Hamiltonian in disordered superlattices, and showing that it can be mapped exactly to a disordered 1D Bose-Hubbard model with a known MBL phase. Our work provides momentum-resolved experimental evidence of phonon localization, extending the scope of MBL to disordered solid-state systems.

cond-mat.mtrl-sci↗

arXiv2021

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.

cond-mat.mtrl-sci↗

arXiv2021

Direct prediction of phonon density of states with Euclidean neural networks

Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here we demonstrate the direct prediction of phonon density of states using only atomic species and positions as input. We apply Euclidean neural networks, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high-quality prediction using a small training set of $\sim 10^{3}$ examples with over 64 atom types. Our predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements,and is naturally suited to efficiently predict alloy systems without additional computational cost. We demonstrate the potential of our network by predicting a broad number of high phononic specific heat capacity materials. Our work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high-performance thermal storage materials and phonon-mediated superconductors.

physics.comp-ph↗

arXiv2020

Quantized Thermoelectric Hall Effect Induces Giant Power Factor in a Topological Semimetal

Thermoelectrics are promising by directly generating electricity from waste heat. However, (sub-)room-temperature thermoelectrics have been a long-standing challenge due to vanishing electronic entropy at low temperatures. Topological materials offer a new avenue for energy harvesting applications. Recent theories predicted that topological semimetals at the quantum limit can lead to a large, non-saturating thermopower and a quantized thermoelectric Hall conductivity approaching a universal value. Here, we experimentally demonstrate the non-saturating thermopower and quantized thermoelectric Hall effect in the topological Weyl semimetal (WSM) tantalum phosphide (TaP). An ultrahigh longitudinal thermopower Sxx= 1.1x10^3 muV/K and giant power factor ~525 muW/cm/K^2 are observed at ~40K, which is largely attributed to the quantized thermoelectric Hall effect. Our work highlights the unique quantized thermoelectric Hall effect realized in a WSM toward low-temperature energy harvesting applications.

cond-mat.mes-hall↗

arXiv2020

Understanding disorder in 2D materials: the case of carbon doping of Silicene

We investigate the effect of lattice disorder and local correlation effects in finite and periodic silicene structures caused by carbon doping using first-principles calculations. For both finite and periodic silicene structures, the electronic properties carbon-doped monolayers are dramatically changed by controlling the doping sites in the structures, which is related to the amount of disorder introduced in the lattice and electron-electron correlation effects. By changing the position of the carbon dopants, we found that a Mott-Anderson transition is achieved. Moreover, the band gap is determined by the level of lattice disorder and electronic correlation effects. Finally, these structures are ferromagnetic even under disorder which has potential applications in Si-based nanoelectronics, such as field-effect transistors (FETs).

cond-mat.mtrl-sci↗

arXiv2020

Topological Singularity Induced Chiral Kohn Anomaly in a Weyl Semimetal

The electron-phonon interaction (EPI) is instrumental in a wide variety of phenomena in solid-state physics, such as electrical resistivity in metals, carrier mobility, optical transition and polaron effects in semiconductors, lifetime of hot carriers, transition temperature in BCS superconductors, and even spin relaxation in diamond nitrogen-vacancy centers for quantum information processing. However, due to the weak EPI strength, most phenomena have focused on electronic properties rather than on phonon properties. One prominent exception is the Kohn anomaly, where phonon softening can emerge when the phonon wavevector nests the Fermi surface of metals. Here we report a new class of Kohn anomaly in a topological Weyl semimetal (WSM), predicted by field-theoretical calculations, and experimentally observed through inelastic x-ray and neutron scattering on WSM tantalum phosphide (TaP). Compared to the conventional Kohn anomaly, the Fermi surface in a WSM exhibits multiple topological singularities of Weyl nodes, leading to a distinct nesting condition with chiral selection, a power-law divergence, and non-negligible dynamical effects. Our work brings the concept of Kohn anomaly into WSMs and sheds light on elucidating the EPI mechanism in emergent topological materials.

cond-mat.mtrl-sci↗

arXiv2020

Large nonreciprocal absorption and emission of radiation in type-I Weyl semimetals with time reversal symmetry breaking

The equality between the spectral, directional emittance and absorptance of an object under local thermal equilibrium is known as Kirchhoff's law of radiation. The breakdown of Kirchhoff's law of radiation is physically allowed by breaking time reversal symmetry and can open opportunities for nonreciprocal light emitters and absorbers. Large anomalous Hall conductivity and angle recently observed in topological Weyl semimetals, particularly type-I magnetic Weyl semimetals and type-II Weyl semimetals, are expected to create large nonreciprocal electromagnetic wave propagation. In this work, we focus on type-I magnetic Weyl semimetals and show via modeling and simulation that nonreciprocal surface plasmons polaritons can result in pronounced nonreciprocity without an external magnetic field. The modeling in this work begins with a single pair of Weyl nodes, followed by a more realistic model with multiple paired Weyl nodes. Fermi-arc surface states are also taken into account through the surface conductivity. This work points to the promising applicability of topological Weyl semimetals for magneto-optical and energy applications.

cond-mat.mes-hall↗