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A transition between two types of step alignment was observed in a multilayered InGaAs/GaAs quantum-dot (QD) structure. A change to larger QD sizes in smaller concentrations occurred after formation of a dislocation array.
Inorganic semiconductor materials are best known for their superior physical properties, as well as their structural rigidity and stability. However, the poor solubility and solution-processability of these covalently bonded network structures has long been a serious drawback that limits their use in many important applications. Here, we present a unique and general approach to synthesize robust, solution-processable, and highly luminescent hybrid materials built on periodic and infinite inorganic modules. Structure analysis confirms that all compounds are composed of one-dimensional anionic chains of copper iodide (Cu m I m+2 2- ) coordinated to cationic organic ligands via Cu-N bonds. The choice of ligands plays an important role in the coordination mode (μ 1 -MC or μ 2 -DC) and Cu-N bond strength. Greatly suppressed nonradiative decay is achieved for the μ 2 -DC structures. Record high quantum yields of 85% (λ ex = 360 nm) and 76% (λ ex = 450 nm) are obtained for an orange-emitting 1D-Cu 4 I 6 (L 6 ). Temperature dependent PL measurements suggest that both phosphorescence and thermally activated delayed fluorescence contribute to the emission of these 1D-AIO compounds, and that the extent of nonradiative decay of the μ 2 -DC structures is much less than that of the μ1 -DC structures. More significantly, all compounds are remarkably soluble in polar aprotic solvents, distinctly different from previously reported CuI based hybrid materials made of charge-neutral Cu m X m (X = Cl, Br, I), which are totally insoluble in all common solvents. Finally, the greatly enhanced solubility is a result of incorporation of ionic bonds into extended covalent/coordinate network structures, making it possible to fabricate large scale thin films by solution processes
Extracting parton structure from lattice quantum chromodynamics (QCD) calculations requires studying the coordinate scale 𝑧 3 dependence of the matrix elements of bilocal operators. The most significant contribution comes from the 𝑧 3 dependence induced by ultraviolet (UV) renormalization of the Wilson line. We demonstrate that the next-to-leading order perturbative calculations of the renormalization factor can describe, to a few percent accuracy, the logarithm of the lattice QCD rest frame matrix elements with separations up to distances of 0.6 fm on multiple lattice spacings. The residual discrepancies can be modeled by a leading effect from the structure of the nucleon.
In nodal-line semimetals, linearly dispersing states form Dirac loops in the reciprocal space with a high degree of electron-hole symmetry and a reduced density of states near the Fermi level. The result is reduced electronic screening and enhanced correlations between Dirac quasiparticles. In this study we investigate the electronic structure of ZrSiSe, by combining time- and angle-resolved photoelectron spectroscopy with ab initio density functional theory (DFT) complemented by an extended Hubbard model ( DFT + U + V ) and by time-dependent DFT + U + V . We show that electronic correlations are reduced on an ultrashort timescale by optical excitation of high-energy electrons-hole pairs, which transiently screen the Coulomb interaction. Our findings demonstrate an all-optical method for engineering the band structure of a quantum material.
Here, the accurate mapping of potential energy surfaces (PESs) is crucial to our understanding of the numerous physical and chemical processes mediated by atomic rearrangements, such as conformational changes and chemical reactions, and the thermodynamic and kinetic feasibility of these processes. Stochastic electronic structure theories, e.g., Quantum Monte Carlo (QMC) methods, enable highly accurate total energy calculations that in principle can be used to construct the PES. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method, previously developed for QMC structural optimization, to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and at saddle points, we show that it is possible to identify MEPs and TSs by using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia (NH 3 ) molecule and the nucleophilic substitution (S N 2) reaction F – + CH 3 F → FCH 3 + F – . We validate our results using Density Functional Theory (DFT)- and Coupled Cluster (CCSD, CCSD(T))-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities, free energy differences, rate constants, and equilibrium constants that incorporates stochastically optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations at significantly reduced computational expense.
Engineering materials that control quantum many-body dynamics remains challenging, as multiparticle interactions typically produce complex emergent behaviour that is difficult to predict. Here we introduce quantum statistical plasmonic metacrystals, structures in which the multiparticle dynamics mediated by optical near fields produce forbidden quantum statistical bands that enable selective transmission of different types of light. This functionality arises from a plasmonic structure composed of nanoantennas acting as meta-atoms. Multiphoton fields with statistics within the allowed bands propagate without distortion, whereas fields in forbidden bands are suppressed or driven towards the nearest accessible statistical state. We show that these bands are determined by the geometry and collective arrangement of the meta-atoms, providing a deterministic route to engineering quantum statistical transport. This platform establishes a room-temperature quantum material intrinsically sensitive to the quantum coherence of many-body photonic systems, enabling their robust manipulation and transport. Our results have implications for coherence-sensitive photonic materials for energy harvesting and scalable many-body quantum technologies.
To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules’ bulk properties of interest a priori to synthesis from a chemical structure alone. In this work, we demonstrate that machine learning methods can indeed be used to directly learn the relationship between chemical structures and bulk crystalline properties of molecules, even in the absence of any crystal structure information or quantum mechanical calculations. We focus specifically on a class of organic compounds categorized as energetic materials called high explosives (HE) and predicting their crystalline density. An ongoing challenge within the chemistry machine learning community is deciding how best to featurize molecules as inputs into machine learning models—whether expert handcrafted features or learned molecular representations via graph-based neural network models—yield better results and why. We evaluate both types of representations in combination with a number of machine learning models to predict the crystalline densities of HE-like molecules curated from the Cambridge Structural Database, and we report the performance and pros and cons of our methods. Our message passing neural network (MPNN) based models with learned molecular representations generally perform best, outperforming current state-of-the-art methods at predicting crystalline density and performing well even when testing on a data set not representative of the training data. However, these models are traditionally considered black boxes and less easily interpretable. Here, to address this common challenge, we also provide a comparison analysis between our MPNN-based model and models with fixed feature representations that provides insights as to what features are learned by the MPNN to accurately predict density.
Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner.
Abstract Quantum chemistry is interested in calculating ground and excited states of molecular systems by solving the electronic Schrödinger equation. The exact numerical solution of this equation, frequently represented as an eigenvalue problem, remains unfeasible for most molecules and requires approximate methods. In this paper we introduce the use of Quantum Community Detection performed using the D-Wave quantum annealer to reduce the molecular Hamiltonian matrix in Slater determinant basis without chemical knowledge. Given a molecule represented by a matrix of Slater determinants, the connectivity between Slater determinants (as off-diagonal elements) is viewed as a graph adjacency matrix for determining multiple communities based on modularity maximization. A gauge metric based on perturbation theory is used to determine the lowest energy cluster. This cluster or sub-matrix of Slater determinants is used to calculate approximate ground state and excited state energies within chemical accuracy. The details of this method are described along with demonstrating its performance across multiple molecules of interest and bond dissociation cases. These examples provide proof-of-principle results for approximate solution of the electronic structure problem using quantum computing. This approach is general and shows potential to reduce the computational complexity of post-Hartree–Fock methods as future advances in quantum hardware become available.
Solving the electronic structure problem of molecules and solids to high accuracy is a major challenge in quantum chemistry and condensed matter physics. The rapid emergence and development of quantum computers offer a promising route to systematically tackle this problem. Recent work by [Huggins et al ., Nature (London) 603 , 416 (2022)] proposed a hybrid quantum-classical quantum Monte Carlo (QC-QMC) algorithm using Clifford shadows to determine the ground state of a Fermionic Hamiltonian. This approach displayed inherent noise resilience and the potential for improved accuracy compared to its purely classical counterpart. Nevertheless, the use of Clifford shadows introduces an exponentially scaling postprocessing cost. In this work, we investigate an improved QC-QMC scheme utilizing the recently developed Matchgate shadows technique [Commun. Math. Phys. 404 , 629 (2023)], which removes the aforementioned exponential bottleneck. We observe from experiments on quantum hardware that the use of Matchgate shadows in QC-QMC is inherently noise robust. We show that this noise resilience has a more subtle origin than in the case of Clifford shadows. Nevertheless, we find that classical postprocessing, while asymptotically efficient, requires hours of runtime on thousands of classical CPUs for even the smallest chemical systems, presenting a major challenge to the scalability of the algorithm.
Tunneling-assisted impact ionization across the conduction-band-edge discontinuity of quantum-well heterostructures is investigated and applied to a new superlattice structure. Multiquantum-well structures where the quantum-well regions are heavily doped and the undoped barrier regions are essentially insulating were considered. Incident hot electrons due to the applied electric field perpendicular to the heterointerface interact with the two-dimensional electrons confined to the quantum wells through Coulomb force. The resultant electrons can either have enough energy to get out of the wells or to tunnel through the triangular barriers. A new analytical approximation for the impact ionization rate is given which compares favorably with numerical results. The tunneling-assisted impact ionization rates and the ionization coefficients are calculated. It is shown that the tunneling effect reduces the ionization threshold and enhances the ionization rate significantly.
In the on-going evolution of GaAs Quantum Well Infrared Photodetectors (QWIPs) we have developed a 1,024 x 1,024 (1K x1K), 8.4-9 microns infrared focal plane array (FPA). This 1 megapixel detector array is a hybrid using the Rockwell TCM 8050 silicon readout integrated circuit (ROIC) bump bonded to a GaAs QWIP array fabricated jointly by engineers at the Goddard Space Flight Center (GSFC) and the Army Research Laboratory (ARL). The finished hybrid is thinned at the Jet Propulsion Lab. Prior to this development the largest format array was a 512 x 640 FPA. We have integrated the 1K x 1K array into an imaging camera system and performed tests over the 40K-90K temperature range achieving BLIP performance at an operating temperature of 76K (f/2 camera system). The GaAs array is relatively easy to fabricate once the superlattice structure of the quantum wells has been defined and grown. The overall arrays costs are currently dominated by the costs associated with the silicon readout since the GaAs array fabrication is based on high yield, well-established GaAs processing capabilities. In this paper we will present the first results of our 1K x 1K QWIP array development including fabrication methodology, test data and our imaging results.
Quantum well intersubband transitions are critical for quantum cascade lasers and infrared photodetectors. Control of band offsets allows bound-to-bound intersubband transitions, with confinement of both initial and final states, and bound-to-continuum transitions, in which only the initial state is energetically confined within the potential well. Both types of transitions are also achieved in colloidal CdSe wells by changing the heterostructure shell. Bare wells have narrow intersubband transitions spanning the near-infrared spectrum following effective mass predictions. Atomically precise core/shells enable a readily adjusted potential well for electrons. For CdSe/ZnS, bound-to-bound transitions are narrow and redshift with shell thickness. By contrast, broad bound-to-continuum absorptions are found in CdSe/CdS. Due to small conduction band offsets, higher conduction band states of the well are more delocalized into the CdS shell. In conclusion, these measurements provide unique data to understand the electronic structure of colloidal quantum wells and chart a path to atomically precise optoelectronic materials for the mid-infrared.
Abstract A compact platform to integrate emitters in a cavity-like support is to embed quantum dots (QDs) in a photonic crystal (PhC) structure, making them promising candidates for integrated quantum photonic circuits. The emission properties of QDs can be modified by tailored photonic structures, relying on the Purcell effect or strong light-matter interactions. However, the effects of photonic states on spatial features of exciton emissions in these systems are rarely explored. Such effect is difficult to access due to random positions of self-assembled QDs in PhC structures, and the fact that quantum well excitons’ wavefunctions resemble photonic states in a conventional distributed Bragg reflector cavity system. In this work, we instead observe a spatial signature of exciton emission using site-controlled QDs embedded in PhC cavities. In particular, we observe the detuning-dependent spatial repulsion of the QD exciton emissions by polarized imaging of the micro-photoluminescence, dependent on the controlled QD’s position in a spatially extended photonic pattern. The observed effect arises due to the quantum interference between QD decay channel in a spatially-extended cavity mode. Our findings suggest that integration of site-controlled QDs in tailored photonic structures can enable spatially distributed single-photon sources and photon switches.
This dataset contains data-intensive quantum chemical electronic structure calculations for 96,766 organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Time Dependent Density Functional Theory (TDDFT) first principles method using the ORCA software. It provides UV-vis spectra calculations of molecules with a high level of accuracy. The optical spectra behavior was collected based on the optimized molecular geometries in the DFTB method with 3ob parameters. All calculations utilized the def2-TZVP basis sets with the auxiliary def2/J and def2-TZVP/C basis sets. The time-dependent density-functional theory (TDDFT) approach with the PBE0 exchange-correlation functional and ORCAs default integration grid was employed. For the excitation energy calculations, the lowest 50 excitation states were calculated.
One of the main loss mechanisms in photovoltaic solar cells is the thermalization of photogenerated hot carriers via phonon-mediated relaxation. By inhibiting these relaxation mechanisms and reducing thermalization losses, it may be possible to improve the power conversion efficiency of solar cells beyond the single gap limit. Here, type-II InAs/AlAsSb multi-quantum well (MQW) structures are investigated to study the impact of the phononic properties of the AlAsSb barrier material in hot carrier thermalization. Experimental and theoretical results show that by increasing the barrier thickness (increasing the relative contribution of AlAsSb content in the superlattices), the relaxation of hot carriers is reduced as observed in power-dependent photoluminescence and thermalization analysis. Furthermore, this is attributed to an increase in the phononic bandgap of the MQW with increasing AlAsSb composition reducing the efficiency of the dominant Klemens mechanism as the phononic properties shift toward a more AlSb-like behavior.
This dataset contains data-intensive quantum chemical electronic structure calculations for 80,593 organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Equation of Motion Coupled Cluster (EOM-CCSD) first principles method using the ORCA software. It provides UV-vis spectra calculations of molecules with a high level of accuracy. The optical spectra behavior was collected based on the optimized molecular geometries in the DFTB method with 3ob parameters. All calculations utilized the def2-TZVP basis sets with the auxiliary def2/J and def2-TZVP/C basis sets. The similarity-transformed EOM-CCSD method that used domain-based local pair natural orbitals (DLPNO) approximation which constitutes the STEOM-DLPNO-CCSD method was used. This method is based on the STEOM approach and was found to make accurate predictions of transition energies for organic molecules. For the excitation energy calculations, the lowest 50 excitation states were calculated.
This project developed a generalizable strategy for optical control of spin-based quantum states through the Photoisomerization-Induced Spin–Charge Excited State (PISCES) process, in which a photochromic spirooxazine ligand bound to an electronically bistable metal complex enables reversible, room-temperature optical switching of spin states in both dilute matrices and the solid state. A multidisciplinary team integrated synthesis, spectroscopy, theory, and surface science to establish design principles for opto-spintronic quantum materials. Systematic structure–property studies across four spirooxazine series revealed that donor–π–acceptor interactions govern thermal isomerization, which proceeds via a rotational pathway. Pulsed EPR measurements demonstrated millisecond-scale coherence times in cobalt-dioxolene-spirooxazine complexes, establishing their promise as optically addressable molecular qubits; the approach was extended to Cu(II), Ni(II), and lanthanide complexes, including optical modulation of zero-field splittings and clock transitions. Two-dimensional IR spectroscopy resolved picosecond ground-state population exchange dynamics, while ab initio calculations elucidated photoisomerization mechanisms and parametrized Hamiltonians for spin relaxation and decoherence. Toward device integration, the team fabricated 2D polymer scaffolds for covalent qubit organization, developed tip-enhanced Raman imaging of molecular monolayers, and demonstrated optical gating of gate voltage in spirooxazine-graphene field-effect transistors. The project produced numerous publications, dissertations, and presentations, and trained over 30 researchers.