Artificial Intelligence and QM/MM with a Polarizable Reactive Force Field for Next-Generation Electrocatalysts
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36 MATERIALS SCIENCE↗
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A density-functional theory study of the Al/AlOx/Al tunnel junction
The aluminum oxide tunnel junction is a key component of the majority of superconducting quantum devices. For high-quality, reproducible, and scalably manufacturable qubits, the ability to fabricate Josephson junctions (JJs) with a targeted critical current and high uniformity is essential. In this study, we use first-principles modeling to assess fundamental aspects of the atomic structure of both amorphous and crystalline aluminum oxide tunnel junctions and relate the structure to predicted performance metrics. We use modified ab initio molecular dynamics to develop realistic models of the tunnel junction, from which interface roughness and local thickness fluctuations are analyzed in an unbiased manner by training a neural network to identify the boundary between metal and oxide. We show that the effective thickness of the insulating part of the junction can be different from the apparent physical thickness. We calculate the rate of Cooper pair tunneling for the atomically resolved electrostatic potential using direct numerical solution in 3D, which shows a channeling effect that impacts the junction critical current. The predicted critical current is a useful JJ design parameter that can be accessed from the ab initio calculations without fitting parameters. To assess the limits of uniformity and fabrication choices (e.g., oxidation vs epitaxy), we compare the amorphous junctions to crystalline models, which show order of magnitude more efficient tunneling compared to the amorphous case, underlining the connection between atomistic structure and Cooper pair tunneling efficiency. Further, this work provides a foundation for ab initio materials design and evaluation to help accelerate future development of improved tunnel junctions.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Spin-orbit-proximitized ferromagnetic metal by monolayer transition metal dichalcogenide: Atlas of spectral functions, spin textures, and spin-orbit torques in Co/MoSe 2 , Co/WSe 2 and Co/TaSe 2 heterostructures
In this work, the heterostructures composed of an ultrathin ferromagnetic metal (FM) and a material hosting strong spin-orbit (SO) coupling are the principal resource for SO torque and spin-to-charge conversion nonequilibrium effects in spintronics. The key quantity in theoretical description of these effects is nonequilibrium spin density, which can appear on any monolayer of the heterostructure through which the current is flowing as long as the monolayer bands are affected by the native or proximity induced SO coupling. Here we demonstrate how hybridization of wave functions of Co layer and a monolayer of transition metal dichalcogenides (TMDs)—such as semiconducting MoSe 2 and WSe 2 or metallic TaSe 2 —can lead to dramatic transmutation of electronic and spin structure of Co within some distance away from its interface with TMD, when compared to the bulk of Co or its surface in contact with a vacuum. This is due to proximity induced SO splitting of Co bands encoded in the spectral functions and spin textures on its monolayers, which we obtain using noncollinear density functional theory (ncDFT) combined with equilibrium Green function (GF) calculations. In fact, SO splitting is present due to structural inversion asymmetry of the bilayer—i.e., just the presence of the interface—even if SO coupling within TMD monolayer is artificially switched off in ncDFT calculations. However, switching it on makes the effects associated with proximity SO coupling within Co layer about five times larger. Injecting spin-unpolarized charge current through SO-proximitized monolayers of Co generates nonequilibrium spin density over them, so that its cross product with the magnetization of Co determines SO torque. The SO torque computed via first-principles quantum transport methodology, which combines ncDFT with nonequilibrium GF calculations, can be used as the screening parameter to identify optimal combination of materials and their interfaces for applications in spintronics. In particular, we identify heterostructure two-monolayer-Co/monolayer-WSe 2 as the most optimal one, at least in the clean limit.
2-dimensional systems↗
Deep learning at the edge enables real-time streaming ptychographic imaging
Abstract Coherent imaging techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent imaging methods like ptychography are poised to revolutionize nanoscale materials characterization. However, these advancements are accompanied by significant increase in data and compute needs, which precludes real-time imaging, feedback and decision-making capabilities with conventional approaches. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the oversampling constraints, allowing low-dose imaging using orders of magnitude less data than required by traditional methods.
36 MATERIALS SCIENCE↗
Machine Learning for Optical Scanning Probe Nanoscopy
Abstract The ability to perform nanometer‐scale optical imaging and spectroscopy is key to deciphering the low‐energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering‐type scanning near‐field optical microscopy (s‐SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s‐SNOM, together with scanning probe research in general, can benefit in many ways from artificial‐intelligence (AI) and machine‐learning (ML) algorithms. Augmented with AI‐ and ML‐enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.
Chen, Xinzhong↗
Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li 7 La 3 Zr 2 O 12 solid electrolytes
Batteries based on solid-state electrolytes, including Li 7 La 3 Zr 2 O 12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. Here these examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.
25 ENERGY STORAGE↗
Large-scale integration of artificial atoms in hybrid photonic circuits
A central challenge in developing quantum computers and long-range quantum networks is the distribution of entanglement across many individually controllable qubits. Colour centres in diamond have emerged as leading solid-state ‘artificial atom’ qubits because they enable on-demand remote entanglement, coherent control of over ten ancillae qubits with minute-long coherence times and memory-enhanced quantum communication. A critical next step is to integrate large numbers of artificial atoms with photonic architectures to enable large-scale quantum information processing systems. So far, these efforts have been stymied by qubit inhomogeneities, low device yield and complex device requirements. Here we introduce a process for the high-yield heterogeneous integration of ‘quantum microchiplets’—diamond waveguide arrays containing highly coherent colour centres—on a photonic integrated circuit (PIC). We use this process to realize a 128-channel, defect-free array of germanium-vacancy and silicon-vacancy colour centres in an aluminium nitride PIC. Photoluminescence spectroscopy reveals long-term, stable and narrow average optical linewidths of 54 megahertz (146 megahertz) for germanium-vacancy (silicon-vacancy) emitters, close to the lifetime-limited linewidth of 32 megahertz (93 megahertz). We show that inhomogeneities of individual colour centre optical transitions can be compensated in situ by integrated tuning over 50 gigahertz without linewidth degradation. The ability to assemble large numbers of nearly indistinguishable and tunable artificial atoms into phase-stable PICs marks a key step towards multiplexed quantum repeaters and general-purpose quantum processors
36 MATERIALS SCIENCE↗
Ice, glass, and solid phases in artificial spin systems with quenched disorder
We present a numerical study on a disordered artificial spin-ice system, which interpolates between the long-range ordered square ice and the fully degenerate shakti ice. Starting from the square-ice geometry, disorder is implemented by adding vertical/horizontal magnetic islands to the center of some randomly chosen square plaquettes of the array at different densities. When no island is added, we have ordered square ice. When all square plaquettes have been modified, we obtain shakti ice, which is disordered yet in a topological phase corresponding to the Rys F-model. In between, geometrical frustration due to these additional center spins disrupts the long-range Ising order of square ice, giving rise to a spin-glass regime at low temperatures. The artificial spin system proposed in our work provides an experimental platform to study the interplay between quenched disorder and geometrical frustration.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Kagome qubit ice
Abstract Topological phases of spin liquids with constrained disorder can host a kinetics of fractionalized excitations. However, spin-liquid phases with distinct kinetic regimes have proven difficult to observe experimentally. Here we present a realization of kagome spin ice in the superconducting qubits of a quantum annealer, and use it to demonstrate a field-induced kinetic crossover between spin-liquid phases. Employing fine control over local magnetic fields, we show evidence of both the Ice-I phase and an unconventional field-induced Ice-II phase. In the latter, a charge-ordered yet spin-disordered topological phase, the kinetics proceeds via pair creation and annihilation of strongly correlated, charge conserving, fractionalized excitations. As these kinetic regimes have resisted characterization in other artificial spin ice realizations, our results demonstrate the utility of quantum-driven kinetics in advancing the study of topological phases of spin liquids.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Artificial-Intelligence Aided Design and Synthesis of Novel Layered 2D Multi-Principal Element Materials for Energy Storage (Final Report)
This DOE-EPSCoR project aimed to predict, synthesize, and characterize novel layered two-dimensional (2D) high-entropy materials (HEMs). These 2D-HEMs, composed of multiple principal elements in nearly equal concentrations, are distinct from traditional 2D materials (typically containing two or three elements) and conventional alloys (dominated by a single primary element with minor secondary additions). Their unique structural and compositional features enable significant lattice strain accommodation, resulting in enhanced electrode performance and potential applications in catalysis, hydrogen storage, sensing, quantum information technologies, and flexible electronics. The research focused on addressing four fundamental questions: (i) What combinations of elements can form stable and synthesizable 2D-HEMs? (ii) What mechanisms drive the stability and synthesizability of crystalline single-phase 2D-HEMs? (iii) How do local chemical disorder and defects influence the macroscopic electronic and mechanical properties? and (iv) What charge storage mechanisms are active in selectively synthesized 2D-HEMs for battery and supercapacitor electrode applications? To achieve these goals, the project employed an integrated theory-experiment approach, incorporating high-throughput first-principles calculations, theoretical modeling, data mining, experimental synthesis, and advanced characterization techniques. The advanced computing resources and state-of-the-art experimental characterization facilities at Oak Ridge National Laboratory (ORNL) were leveraged through collaboration. Beyond scientific advancements, the project contributed to workforce development. Two postdoctoral researchers and three graduate students at the University of Maine were trained through co-advising by ORNL scientists and collaborative interactions, strengthening their expertise in cutting-edge materials science.
36 MATERIALS SCIENCE↗
Identification of high-dielectric constant compounds from statistical design
Abstract The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < ϵ < 101) and large band gaps (2.9 < E g (eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu 5 SiCl 6 O 4 and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 < ϵ < 40) and band gaps (2.3 < E g (eV) < 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities.
36 MATERIALS SCIENCE↗
An integrated photonic engine for programmable atomic control
Abstract Solutions for scalable, high-performance optical control are important for the development of scaled atom-based quantum technologies. Modulation of many individual optical beams is central to applying arbitrary gate and control sequences on arrays of atoms or atom-like systems. At telecom wavelengths, miniaturization of optical components via photonic integration has pushed the scale and performance of classical and quantum optics far beyond the limitations of bulk devices. However, material platforms for high-speed telecom integrated photonics lack transparency at the short wavelengths required by leading atomic systems. Here, we propose and implement a scalable and reconfigurable photonic control architecture using integrated, visible-light modulators based on thin-film lithium niobate. We combine this system with techniques in free-space optics and holography to demonstrate multi-channel, gigahertz-rate visible beamshaping. When applied to silicon-vacancy artificial atoms, our system enables the spatial and spectral addressing of a dynamically-selectable set of these stochastically-positioned point emitters.
Science & Technology - Other Topics↗
Persistent dynamic magnetic state in artificial honeycomb spin ice
Abstract Topological magnetic charges, arising due to the non-vanishing magnetic flux on spin ice vertices, serve as the origin of magnetic monopoles that traverse the underlying lattice effortlessly. Unlike spin ice materials of atomic origin, the dynamic state in artificial honeycomb spin ice is conventionally described in terms of finite size domain wall kinetics that require magnetic field or current application. Contrary to this common understanding, here we show that a thermally tunable artificial permalloy honeycomb lattice exhibits a perpetual dynamic state due to self-propelled magnetic charge defect relaxation in the absence of any external tuning agent. Quantitative investigation of magnetic charge defect dynamics using neutron spin echo spectroscopy reveals sub-ns relaxation times that are comparable to the relaxation of monopoles in bulk spin ices. Most importantly, the kinetic process remains unabated at low temperature where thermal fluctuation is negligible. This suggests that dynamic phenomena in honeycomb spin ice are mediated by quasi-particle type entities, also confirmed by dynamic Monte-Carlo simulations that replicate the kinetic behavior. Our research unveils a macroscopic magnetic particle that shares many known traits of quantum particles, namely magnetic monopole and magnon.
36 MATERIALS SCIENCE↗
Radiation-Induced Dark Counts for Silicon Single-Photon Detectors in Space
Single-photon detectors operating on satellites for use in a quantum communications network can incur large dark-count-rate increases from the natural radiation environment of space. Displacement damage to the material lattice of a detector from the ionizing radiation can result in a permanent dark-count increase in the detector. In this work, we analyze the radiation-induced dark-count rate of a silicon single-photon avalanche diode onboard a satellite at different orbiting altitudes, as well as, the additional radiation from a nuclear-disturbed environment caused by a high-altitude nuclear explosion. For detectors on low Earth orbit satellites, protons are the biggest source of radiation damage and are best mitigated by choosing an orbit that minimizes exposure when passing through the South Atlantic Anomaly and Polar Cusps. Detectors on medium Earth orbit and geostationary orbit satellites, if shielded by more than 10 mm of aluminum, provide the best platform in terms of the least amount of radiation damage to the detectors. In the event of a high-altitude nuclear explosion, the artificial radiation belts produced by the explosion will cause too much damage to silicon single-photon detectors on low Earth orbit satellites and render them unfit for quantum communications in less than a day. Higher orbit satellites will only suffer minor dark-count-rate increases from the artificial radiation belts.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Spectroscopy of the fractal Hofstadter energy spectrum
Hofstadter’s butterfly, the predicted energy spectrum for non-interacting electrons confined to a two-dimensional lattice in a magnetic field, is one of the most remarkable fractal structures in nature. At rational ratios of magnetic flux quanta per lattice unit cell, this spectrum shows self-similar distributions of energy levels that reflect its recursive construction. For most materials, Hofstadter’s butterfly is predicted under experimental conditions that are unachievable using laboratory-scale magnetic fields. More recently, electrical transport studies have provided evidence for Hofstadter’s butterfly in materials engineered to have artificially large lattice constants, such as those with moiré superlattices. Yet, so far, direct spectroscopy of the fractal energy spectrum predicted by Hofstadter nearly 50 years ago has remained out of reach. Here, in this work, we use high-resolution scanning tunnelling microscopy/spectroscopy (STM/STS) to investigate the flat electronic bands in twisted bilayer graphene (TBG) near the predicted second magic angle, an ideal setting for spectroscopic studies of Hofstadter’s spectrum. Our study shows the fractionalization of flat moiré bands into discrete Hofstadter subbands and discerns experimental signatures of self-similarity of this spectrum. Moreover, our measurements uncover a spectrum that evolves dynamically with electron density, showing phenomena beyond that of Hofstadter’s original model owing to the combined effects of strong correlations, Coulomb interactions and the quantum degeneracy of electrons in TBG.
electronic properties and materials↗
Tailoring magnetization reversal of a single-domain bar nanomagnet via its end geometry
Nanoscale single-domain bar magnets are building blocks for a variety of fundamental and applied mesoscopic magnetic systems, such as artificial spin ices, magnetic shape-morphing microbots, and magnetic majority logic gates. The magnetization reversal switching field of the bar nanomagnets is a crucial parameter that determines the physical properties and functionalities of their constituted artificial systems. Previous methods on tuning the magnetization reversal switching field of a bar nanomagnet usually relied on modifying its aspect ratio, such as its length, width, and/or thickness. Here, we show that the switching field of a bar nanomagnet saturates when extending its length beyond a certain value, preventing further tailoring of the magnetization reversal via aspect ratios. We showcase a highly tunable switching field of a bar nanomagnet by tailoring its end geometry without altering its size. This provides an easy method to control the magnetization reversal of a single-domain bar nanomagnet. It would enable new research and/or applications, such as designing artificial spin ices with additional tuning parameters, engineering magnetic microbots with more flexibility, and developing magnetic quantum-dot cellular automata systems for low power computing.
36 MATERIALS SCIENCE↗
Evolution from Tunneling to Hopping Mediated Triplet Energy Transfer from Quantum Dots to Molecules
Efficient energy transfer is particularly important for multiexcitonic processes like singlet fission and photon upconversion. Observation of the transition from short-range tunneling to long-range hopping during triplet exciton transfer from CdSe nanocrystals to anthracene is reported here. This is firmly supported by steady- state photon upconversion measurements, a direct proxy for the efficiency of triplet energy transfer (TET), as well as transient absorption measurements. When phenylene bridges are initially inserted between a CdSe nanocrystal donor and anthracene acceptor, the rate of TET decreases exponentially, commensurate with a decrease in the photon upconversion quantum efficiency from 11.6% to 4.51% to 0.284%, as expected from a tunneling mechanism. However, as the rigid bridge is increased in length to 4 and 5 phenylene units, photon upconversion quantum efficiencies increase again to 0.468% and 0.413%, 1.5–1.6 fold higher than that with 3 phenylene units (using the convention where the maximum upconversion quantum efficiency is 100%). This suggests a transition from exciton tunneling to hopping, resulting in relatively efficient and distance-independent TET beyond the traditional 1 nm Dexter distance. Transient absorption spectroscopy is used to confirm triplet energy transfer from CdSe to transmitter, and the formation of a bridge triplet state as an intermediate for the hopping mechanism. Lastly, this first observation of the tunneling-to-hopping transition for long-range triplet energy transfer between nanocrystal light absorbers and molecular acceptors suggests that these hybrid materials should further be explored in the context of artificial photosynthesis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Frontiers in computing for artificial intelligence
An emerging diversity of computational platforms offers many different approaches to adopting the paradigm of artificial intelligence to the study of electron-ion collisions. Here we review several leading candidates in this computational frontier and their workflows for experimental applications of artificial intelligence that may impact the future Electron-Ion Collider. We discuss the motivation for exploring novel methods to solve artificial intelligence and machine learning problems including with customized devices, quantum simulation, and heterogeneous computing systems. Furthermore, these technologies offer promising approaches to address some of the leading concerns of future computing that may impact the Electron-Ion Collider but they will require further development and testing in order to support future planning efforts.
detector design and construction technologies and ↗