Recently, machine learning tools have been used to aid in the search for novel materials with desirable properties. Materials informatics – the combination of machine learning with materials science – is a promising area of research which opens up new avenues for materials discovery and the unearthing of physical insights. In this talk, we will use materials informatics to search for new two-dimensional (2D)magnetic materials. The recent discovery of intrinsic ferromagnetism in monolayer CrI3 and bilayer Cr2Ge2Te6 created great interest in 2D materials with intrinsic magnetic
order. How many of these materials exist? What are their properties? We use materials informatics to study the magnetic and thermodynamic properties of 2D materials. Crystal structures based on monolayer Cr2Ge2Te6, of the form A2B2X6, are studied using density functional theory (DFT) calculations and machine learning tools. Magnetic properties, such as the magnetic moment are determined. The formation energies are also calculated and used to estimate the chemical stability. We show that machine learning, combined with DFT, provides a computationally efficient means to predict properties of two-dimensional (2D) magnets. In addition, data analytics provides insights into the microscopic origins of magnetic ordering in 2D. This non-traditional approach to materials research paves the way for the rapid discovery of chemically stable 2D magnetic materials.
Topological defects in ferroic order are extensively studied as templates for unique physical phenomena and in the design of low-dimensional, reconfigurable functional elements such as high-density memory “bits.” Since such defects may offer localized non-bulk properties and low dissipative spatial controllability within a chemically homogenous nanoscale medium, the ability to noninvasively detect and probe the 3D morphology and dynamic of ferroelectric vortex-core in operando, with sub-nanometre precision remains a daunting experimental task. Here, we develop and demonstrate the applicability of X-ray Bragg coherent diffractive imaging (BCDI) to address these challenges in individual ferroelectric and multiferroic nanocrystals. Applicability of BCDI to a wider class of systems are discussed.
Emerging quantum materials, such as novel two-dimensional (2D) materials and topologically nontrivial materials, have gained increasing attention due to their unique electronic and photonic properties. The realization of the optoelectronic applications of these materials still faces several challenges. For example, it is critical to gain clear understandings of (1) the fundamental light-matter interactions, which govern many of the key material properties, and (2) the coupling with other nanostructures, which is a required structure for devices and systems. This talk introduces new discoveries and pioneer work using optical spectroscopic techniques on these critical challenges, and novel applications of 2D materials in sensing. The first part of this talk presents the essential material properties investigated using spectroscopy, including interlayer coupling of Moirè patterns of 2D materials, and anisotropic light-matter interactions of 2D materials and Weyl semimetals. The second part of this talk focuses on the interaction of 2D materials with other nanostructures and the related applications. The interactions of 2D materials and selected organic molecules revealed novel enhancement effect of Raman signals for molecules on 2D surface, which offers a new paradigm in biochemical sensing. The works presented in this talk are significant in fundamental nanoscience, and offer important guidelines for practical applications in optoelectronics, sensing, and quantum technologies. The methodologies used here also provide a framework for the future study of many new quantum materials.
High-energy gamma-ray observations are an essential probe of cosmic-ray acceleration mechanisms. The detection of the highest energy gamma rays and the shortest timescales of variability are the key to improve our understanding of the acceleration processes and the environment of the cosmic accelerators.
The High Altitude Water Cherenkov (HAWC) experiment is a large field of view, multi-TeV, gamma-ray observatory continuously operating at 14,000 ft since March, 2015. The HAWC observatory has an order of magnitude better sensitivity, angular resolution, and background rejection than the previous generation of water-Cherenkov arrays. The improved performance allows us to discover TeV sources, to detect transient events, to study the Galactic diffuse emission at TeV energies, and to measure or constrain the TeV spectra of GeV gamma-ray sources. In addition, HAWC is the only ground-based instrument capable of detecting prompt emission from gamma-ray bursts above 100 GeV.
In this colloquium I will present the most recent results using the first three years of data from the HAWC observatory. I will also briefly mention the exciting perspectives of building a next-generation gamma-ray experiment at very high altitude in the Southern Hemisphere.
Interaction of light with matter lies at the heart of a plethora of fundamental phenomena and technological applications. The strength of this interaction can be controlled by engineering the electromagnetic environment surrounding the matter. In this talk I will discuss the ongoing work in my group to explore emergent material properties that arise through the coherent interaction between material excitations and artificially engineered electromagnetic media. This work is motivated by the quest to understand the ultimate limits of controlling optical transitions, carrier transport, energy harvesting, nonlinear optical response and quantum effects. Specifically, we will discuss the regimes of weak and strong light-matter interactions and their implication on the material response. I will begin with a discussion of strong light-matter coupling realized by embedding two-dimensional materials in a photon box (a.k.a optical cavity) [Finally, I will briefly talk about defect engineering in van der Waals materials as a means to realize deterministic quantum emitters [Optica 5, 1128 (2018)] and approaches to couple their emission to high finesse optical cavities. These four example topics all have the underlying theme of controlling light-matter interaction at the nanoscale as a means for engineering matter to realize emergent optoelectronic properties., 30 (2015)] and approaches to control them optically [Nature Photon. 11, 491 (2017)] and electrically. Following this, I will present results on modifying properties such as energy transfer and luminescence of organic molecular materials through strong light-matter coupling [PNAS 116, 5214 (2019)]. In the second part of the talk, I will discuss the regime of weak light-matter coupling to enhance luminescence from low-dimensional semiconductors by designing artifical optical media based on ideas from topology [Science 336, 205 (2012); PNAS 114, 5125 (2017)].
Plasmonic nanostructures have long been appreciated for their ability to harvest photon energy and transform it into other forms, including chemical energy (through the production of hot charge carriers) and thermal energy. Hot carrier production has enabled a variety of photoelectrochemical reactions at plasmonic nanoparticle electrodes, driven by either hot electrons or hot holes. However, at the same time, thermal energy increases mass transport of reactants to the surface and can shift the standard potential of electrochemical reactions, further impacting the rate of photoelectrochemical reactions. Decoupling the relative contributions of plasmon-mediated local heating from hot carrier effects on these reactions has long been an experimental challenge, because we do not have the ability to control each plasmon decay pathway independently. Moreover, plasmon-generated hot carriers quickly lose their energy due to collisions, generating a distribution of hot carrier energies with varying oxidizing or reducing power. This talk will describe our work to isolate local heating effects from hot carrier effects as well as provide quantitative values for hot carrier energies using scanning electrochemical microscopy (SECM) on gold nanoparticles at semiconductor interfaces. We generate real temperature values as well as effective hot carrier temperatures, allowing us to understand how plasmon excitation promotes reactions on plasmon substrates.
Pearl Jam’s hit, “The Light Years,” declares “We were but stones, light made us stars.” Nanophotonic materials and methods promise to elucidate many unknown dynamic processes at the molecular and nanoscale, provided they can ‘shine’ in reactive environments. Here we present our research developing nanophotonic techniques for dynamic, in-situ imaging of photocatalysis, single cell processes, and in-vivo force transduction at the nanoscale. First, we present methods to visualize plasmon-induced chemical transformations with sub-2nm spatial resolution. Our goal is to help unravel the means by which plasmons mediate and control the local chemistry, and ultimately, use that knowledge to optimize photocatalyst performance. As a model reaction, we study the gas-phase photocatalytic dehydrogenation of Au-Pd systems, in which the Au acts as a plasmonic light absorber and Pd serves as the catalyst. Under controlled hydrogen pressures, temperatures, and illumination conditions, we study the study the kinetics of the desorption reaction triggered by the optical excitation of plasmons. We find that plasmons increase the overall rate more than ten-fold and open a new reaction pathway that is not observed without illumination. These results help elucidate the role of plasmons in light-driven phase transformations, en-route to design of site-selective and product-specific photocatalysts. Second, we combine Raman spectroscopy and deep learning to accurately classify bacteria by both species and antibiotic resistance in a single step. We design a convolutional neural network (CNN) for spectral data and train it to identify 30 of the most common bacterial strains from single-cell Raman spectra, achieving antibiotic treatment identification accuracies exceeding 99% and species identification accuracies similar to leading mass spectrometry identification techniques. Our combined Raman-CNN system represents a proof-of-concept for rapid, culture-free identification of bacteria and their antibiotic resistance. Finally, we introduce a new class of in vivo optical probes to monitor biological forces with high spatial and temporal resolution. Our design is based on upconverting nanoparticles that, when excited in the near-infrared, emit light of a different color and intensity in response to microNewton forces. The nanoparticles are sub-30nm in size, do not bleach or photoblink, and can enable deep tissue imaging with minimal tissue autofluorescence. We present the design, synthesis, and characterization of these nanoparticles both in vitro and in vivo, focusing on the forces generated by the roundworm C. elegans as it feeds and digests its bacterial food. Chronic cytotoxicity assays are used to confirm biocompatibility. Our force measurements are coupled with electrical measurements of muscle contractions in both wild-type and mutant animals, providing insight into the interplay between mechanical, electrical, and chemical signaling in vivo.
Jennifer Dionne is an associate professor of Materials Science and Engineering at Stanford, and an affiliate faculty of the Wu Tsai Neurosciences Institute, TomKat Center for Sustainable Energy, Institute for Immunity, Transplantation, and Infection, and Bio-X. She serves as director of the Department of Energy funded Photonics at Thermodynamic Limits Energy Frontier Research Center and faculty co-director of Stanford’s Photonics Research Center. Jen received her B.S. degrees in Physics and Systems Science and Mathematics from Washington University in St. Louis in 2003 and her Ph. D. in Applied Physics at the California Institute of Technology in 2009, advised by Harry Atwater. Prior to joining Stanford, she served as a postdoctoral researcher in Chemistry at Berkeley, advised by Paul Alivisatos. Jen’s research develops new materials and microscopies to observe chemical and biological processes as they unfold with nanometer scale resolution. She then uses these observations to help improve energy-relevant processes (such as photocatalysis and energy storage) and medical diagnostics. Her work has been recognized with a Moore Inventor Fellowship, the Materials Research Society Young Investigator Award, Adolph Lomb Medal, Sloan Foundation Fellowship, and the Presidential Early Career Award for Scientists and Engineers, and was featured on Oprah’s list of “50 Things that will make you say ‘Wow’!”. When not in the lab, Jen enjoys teaching both undergraduate and graduate classes (including “Waves and Diffraction,” “Materials Chemistry”, “Optoelectronics”, and “Science of the Impossible”), exploring the intersection of art and science, cycling the latest century, and reliving her childhood with her two young sons.
Material informatics is a new initiative which has attracted a lot of attention in recent scientific research. The basic strategy is to construct comprehensive data sets and use machine learning to solve a wide variety of problems in material design and discovery. In pursuit of this goal, a key element is the quality and completeness of the databases used. Recent advance in the development of crystal structure prediction algorithms has made it a complementary and more efficient approach to explore the structure/phase space in materials using computers. In this talk, we discuss the importance of the structural motifs and motif-networks in crystal structure predictions. Correspondingly, powerful methods are developed to improve the sampling of the low-energy structure landscape.
The thermodynamic power conversion efficiency limit for silicon solar cells is close to 33%, while commercially available cells have efficiencies in the 17-20% range. The world record for silicon solar cells has inched upward from 25% to 26.7%, in the past twenty years, using cell thicknesses ranging from 450 microns to 165 microns. Photonic crystal architectures enable broadband light absorption beyond the longstanding Lambertian limit and allow silicon to absorb sunlight nearly as well as a direct-bandgap semiconductor. When combined with state-of-the-art electronics, a technological paradigm shift appears imminent. In this lecture, I describe how wave-interference-based solar light-trapping in realistic photonic crystals can break longstanding barriers, enabling flexible, thin-film, silicon to achieve an unprecedented, single-junction, power conversion of 31% [1, 2].
1. "Towards 30% Power Conversion Efficiency in Thin-Silicon Photonic-Crystal Solar Cells," S. Bhattacharya, I. Baydoun, Mi Lin and Sajeev John, Physical Review Applied, 11, 014005 (2019)
2. “Beyond 30% conversion efficiency in silicon solar cells” S. Bhattacharya and Sajeev John (to be published)
Studying nature directly from fundamental degrees of freedom is often computationally limited by physical characteristics of exponentially growing configuration (Hilbert) spaces with particle number and signal-to-noise problems. This leaves many systems of interest to nuclear and particle physics intractable for known algorithms with current and foreseeable classical computational resources. By leveraging their natural capacity to describe nature, the use of quantum systems themselves to form a computational framework leads to constructions of basic quantum field theories with resource requirements that are expected to scale only polynomially with the precision and size of the system. In this talk, I will present an overview of recent progress in, and the potential for, manipulating controllable quantum devices to pursue computational access to our microscopic descriptions of nature.
Since 2004 two-dimensional (2D) materials including graphene, transition metal dichalcogenides (TMDCs) and their heterostructures have continued to draw intense research world-side due to their fascinating new fundamental science and diverse potential applications. 2D systems contain a very small amount of material and scanning probe microscopy (atomic force microscopy and scanning tunneling microscopy) and transmission electron microscopy are commonly used to study their local structural quality. At Rensselaer we have developed a unique and simple method called azimuthal refection high-energy electron diffraction (ARHEED) to measure the wafer scale structural quality of 2D materials. In this talk, I will introduce the basic principle of ARHEED and its characterization of wafer-scale 2D materials. Several examples including graphene and MoS2 will be presented to illustrate the use of AHREED to probe the large scale integrity of the samples.
Pure carbon structures contain a wealth of information and potential properties, we focus on two, long linear carbon chains and Schwarzites. Long linear carbon chains, a one dimensional sp hybridized carbon chain,
have been observed to be encapsulated by a carbon nanotube. Together this system produces a resonant Raman signal from 1770-1860 cm-1, known as the C-mode. The origin of this signal is still under scrutiny. We explore the nature of Raman activity in long linear carbon chains through the use of first principles density functional theory and identify the effect of exact exchange in calculations using hybrid functionals. With exact exchange the most intense Raman active mode, the longitudinal optical mode, converges, with respect to length, to 1831
cm-1, within the range of reported measurements of the C-mode. Also the electronic gap converges, with respect to length, to 1.8 eV, near the known resonance energy.
Schwarzites are sp2, or hexagonal, triply periodic minimal carbon surfaces with negative Gaussian curvature from the introduction of 7-, 8-, 9-, and 10-membered rings. Recently theoretical impregnation of zeolites, simulating the templating processes, has produced Schwarzites.
We present first principles and classical dynamics calculations of electronic and vibronic density of states calculations to establish a connection between the theoretical structures proposed to the experimentally obtained materials. We identify the theoretical structures as energetically and dynamically stable, graphitic in nature, and semimetallic.
Quantum computers promise to solve problems that are not practically feasible with classical computers, with applications ranging from drug development and quantum chemistry to artificial intelligence and cryptography. In this talk, I will give an overview of the current state of experimental quantum computing, specifically results with superconducting qubits. I will then highlight our work on improving the scalability of superconducting quantum devices by interfacing them with classical superconducting logic. Recent results as well as future experiments will be discussed.
Abstract 1: Adiabatic quantum computers like the D-Wave 2000Q can approximately solve the QUBO problem, which is an NP-Hard problem, and have been shown to outperform classical computers on several instances. Solving the QUBO problem literally means solving virtually any NP-Hard problem like the Traveling Salesman Problem (TSP), Airline Scheduling Problem, Protein Folding Problem, Genotype Imputation Problem etc., thereby enabling significant scientific progress, and potentially saving millions / billions of dollars in logistics, airlines, healthcare and many other industries. However, before QUBO problems are solved on quantum computers, they must be embedded (or compiled) onto the hardware of quantum computers, which in itself is a very hard problem. In this work, we propose an efficient embedding algorithm, that lets us embed QUBO problems fast, uses less qubits and gets the objective function value close to the global minimum value. We then compare the performance of our embedding algorithm to that of D-Wave's embedding algorithm, which is the current state of the art, and show that our embedding algorithm convincingly outperforms D-Wave's embedding algorithm. Our embedding approach works with perfect Chimera graphs, i.e. Chimera graphs with no missing qubits.
Abstract 2: For training unsupervised probabilistic machine learning models, matrix computation and sample generation are the two key steps. While GPUs excel at matrix computation, they use pseudo-random numbers to generate samples. Contrarily, Adiabatic Quantum Processors (AQP) use quantum mechanical systems to generate samples accurately and quickly, but are not suited for matrix computation. We present a Classical-Quantum Hybrid Approach for training unsupervised probabilistic machine learning models, leveraging GPUs for matrix computations and the D-Wave quantum sampling library for sample generation. We compare this approach to classical and quantum approaches across four performance metrics. Our results indicate that while the hybrid approach--which uses one AQP and one GPU--outperforms quantum and one of the classical approaches, it performs comparably to the GPU approach, and is outperformed by the CPU approach, which uses 56 high-end CPUs. Lastly, we compare sampling on AQP versus sampling library and show that AQP performs better.
Two-dimensional atomically-thin materials have received enormous interest as a result of their unique mechanical, electrical and optical properties. As a result of the enhanced light-matter interaction two-dimensional materials support they have been investigated for applications in opto-electronics although not much work has focused on these systems as a platform for quantum photonics and quantum optics. In this talk I will discuss approaches that leverage atomically thin two-dimensional materials, as well as their van der Waals heterostructures, for applications in quantum science. In the first part of the talk I will describe the unique photophysical properties of quantum emitters hosted by these atomically thin materials. I will describe our recent efforts to controllably charge the quantum emitters and realize a localized spin-valley-photon interface. I will also present results on realizing cavity polaritons that are a manifestation of many body physics arising when coupling the atomically thin semiconductor to a planar optical cavity.
Since the discovery of the proton and neutron physicists have been trying to image and understand the nucleon’s structure. Today the nucleon is described in terms of a mix of quarks and antiquarks interacting primarily via the strong nuclear force (quantum chromodynamics or QCD). This talk will briefly review the historical progress with electron accelerators leading to our present view of the nucleon’s structure and the recent experimental and theoretical physics progress, focusing on the exclusive reaction program at the Jefferson Lab electron accelerator facility in Virginia, where the structure of the nucleon is a primary subject of the recent and future experimental program.
Fundamental excitations (e.g., excitons, plasmons, phonons, and magnons) determine both the equilibrium and non-equilibrium properties of semiconductors, metals, and insulators. In this talk, I will give a few examples of our recent work in investigating fundamental excitations with various optical spectroscopy methods. I will focus on how excitons (bound electron-hole pairs) and their associated valley index influence light-emitting properties of semiconducting van der Waals monolayers and heterostructures. As a second example, I will discuss how coupling between excitons and surface plasmon polaritons can be used to sort and route valley polarized excitons using a metasurface. Finally, I will briefly discuss how coupling between magnons and phonons in magnetic insulators may lead to new thermoelectric devices.
Neutrinos, the most ghostly of Standard Model particles, were first detected by Clyde Cowan and Frederick Reines in 1956. Although these particles are included in the Standard Model, their properties such as mass and mixing parameters are not predicted and must be measured. Ever since their discovery, physicists have been trying to piece together a comprehensive understanding of the neutrino and over the past 6 decades a nearly complete picture has emerged. However, there are still some undetermined parameters as well as phenomena that have resisted explanation. One of these unexplained phenomena that has arisen rather recently termed the "reactor antineutrino anomaly" is the deficit in the measured flux of antineutrinos from nuclear reactors relative to the expected flux from calculation. Furthermore, the energy spectrum of reactor antineutrinos from recent experiments deviates from the calculated shape in the region of 5-7 MeV antineutrino energy. PROSPECT, the Precision Reactor Oscillation and Spectrum experiment, currently taking data at Oak Ridge National Lab, aims to shed some light on these anomalies by measuring the flux a few meters from the core of a highly enriched uranium (HEU) research reactor. In so doing, many obstacles had to be overcome, not the least of which was designing a detector that could isolate antineutrino events from huge backgrounds inherent in the environment at the surface of the Earth and next to a reactor. In this talk, I will give a brief history of neutrinos and how their properties were determined highlighting some key experiments. Special emphasis will be given to the PROSPECT experiment with recent data and analysis shown.
This year, the Nobel Prize in Physics was awarded to Arthur Ashkin, Gérard Mourou and Donna Strickland for their groundbreaking research leading to optical tweezers and chirped laser pulse amplification. These inventions have revolutionized laser physics. Extremely small objects and incredibly rapid processes are now being seen in a new light. Advanced precision instruments are opening up unexplored areas of research and a multitude of industrial and medical applications.
In this talk, the physical principles behind optical tweezers and chirped laser pulse amplification are explained. The high impact of the inventions and their broad applications today will be discussed.
Recently, experiments with ultracold gases have made it possible to study dynamics of (nearly) isolated many-body quantum systems. This has revived theoretical interest on this topic. In generic isolated systems, one expects nonequilibrium dynamics to result in thermalization: a relaxation to states where the values of macroscopic quantities are stationary, universal with respect to widely differing initial conditions, and predictable through the time-tested recipe of statistical mechanics. However, it is not obvious what feature of a many-body system makes quantum thermalization possible, in a sense analogous to that in which dynamical chaos makes classical thermalization possible. Underscoring that new rules could apply in the quantum case, experimental studies in one-dimensional systems have shown that traditional statistical mechanics can provide wrong predictions for the outcomes of relaxation dynamics. We argue that generic isolated quantum systems do in fact relax to states in which observables are well-described by statistical mechanics. Moreover, we show that time evolution itself plays a merely auxiliary role as thermalization happens at the level of individual eigenstates. We also discuss what happens at integrable points, in which a different set of rules apply.
Significant research and development in the field of Si Photonics have been done in the past two decades leading to the worldwide establishment of Si Photonics foundry services. The technology advancement includes hybrid integration of III-V laser diodes on Si platform, micro-ring or disk resonators, integrated PN junction based Mach-Zehnder (MZ) light modulators, SiGe on Si photodetectors, SiN low-loss waveguides, integrated optical delay line, array waveguide gratings etc. In my group, the current focus of research is slow-light Bragg grating rib waveguides and slot waveguides on Si substrate. Slow-light waveguide has enhanced light-matter interaction so allowing significant size reduction in active photonic devices such as MZ light modulators. The MZ modulator is a basic building block for a large array of tunable optical interference units for the on-chip optical neuromorphic computing application in which the MZ interferometer serves as an arbitrary unitary matrix. The passive slow-light waveguide is also a key element for chip scale photonic system integration in many application areas. For example, the slow-light Bragg grating waveguide has been used to construct tunable optical delay lines for beamforming in phased array antennas. For on-chip optical reservoir computing, a dispersive Bragg grating is a critical element in obtaining nanoseconds of true-time delay for storage of memories.
In this talk, I will start from a general picture of excited states, such as quasiparticles and excitons, in solids and how to calculate them by first-principles approaches. Then I will focus on light-matter interactions of nanoscale materials, in which the reduced dimensionality substantially enhances many-electron interactions by one to two orders of magnitude and results in unique excited-state properties, such as strongly polarized excitons and exciton liquids. By clarifying and calculating electron-electron, electron-hole, and electron-plasmon interactions, respectively, we can accurately explain many important measurements and provide new routes to engineer light-matter interactions for device and energy applications of nanostructures. Finally, taking a new family of two-dimensional materials, black phosphorus and its isoelectronic materials as examples, I will broaden the study by combining different levels of first-principles tools and models to reliably predict a wide range of electronic, optical, thermal, and polarization properties of solids.