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DOE OSTI2024

Time-of-flight detection of terahertz phonon-polariton

A polariton is a fundamental quasiparticle that arises from strong light-matter interaction and as such has attracted wide scientific and practical interest. When light is strongly coupled to the crystal lattice, it gives rise to phonon-polaritons (PPs), which have been proven useful in the dynamical manipulation of quantum materials and the advancement of terahertz technologies. Yet, current detection and characterization methods of polaritons are still limited. Traditional techniques such as Raman or transient grating either rely on fine-tuning of external parameters or complex phase extraction techniques. To overcome these inherent limitations, we propose and demonstrate a technique based on a time-of-flight measurement of PPs. We resonantly launch broadband PPs with intense terahertz fields and measure the time-of-flight of each spectral component with time-resolved second harmonic generation. The time-of-flight information, combined with the PP attenuation, enables us to resolve the real and imaginary parts of the PP dispersion relation. We demonstrate this technique in the van der Waals magnets NiI 2 and MnPS 3 and reveal a hidden magnon-phonon interaction. We believe that this approach will unlock new opportunities for studying polaritons across diverse material systems and enhance our understanding of strong light-matter interaction.

condensed-matter physics↗

DOE OSTI2026

Efficient Berry phase calculation via adaptive variational quantum computing approach

We present an adaptive variational quantum algorithm to estimate the Berry phase accumulated by a nondegenerate ground state under cyclic, adiabatic evolution of a time-dependent Hamiltonian. Our method leverages cyclic adiabatic evolution of the Hamiltonian and employs adaptive variational quantum algorithms for state preparation and evolution, optimizing circuit efficiency while maintaining high accuracy. We benchmark our approach on dimerized Fermi–Hubbard chains with four sites, demonstrating precise Berry phase simulations in both noninteracting and interacting regimes. Our results show that circuit depths reach up to 106 layers for noninteracting systems and increase to 279 layers for interacting systems due to added complexity. In addition, we demonstrate the robustness of our scheme across a wide range of parameters governing adiabatic evolution and variational algorithms. These findings highlight the potential of adaptive variational quantum algorithms for advancing quantum simulations of topological materials and computing geometric phases in strongly correlated systems.

Mootz, Martin [Ames Laboratory (AMES), Ames, IA (U↗

DOE OSTI2025

Multifunctional Polar 2D Lead Iodide Perovskites Exhibiting Persistent Spin Texture

Ferroelectric Rashba semiconductors are a rare class of multifunctional materials promising for spin–orbitronics due to the possibility of electrically switchable spin textures. Hybrid organic–inorganic metal halide perovskites offer exceptional tunability that can be harnessed to target noncentrosymmetry; however, the complex interactions between organic and inorganic components are not rationally understood. Here, we use an asymmetric spacer cation with a strong molecular dipole moment (2-fluorobenzylammonium, 2FBZ) and increase the intrinsic quantum well thickness to n > 1 to synthesize three new polar 2D lead iodide perovskites (2FBZ) 2 (A) n-1 Pb n I 3n+1 with A = methylammonium (MA) or formamidinium (FA) and n = 2, 3. Single-crystal structure analysis reveals the C 2v symmetry of these crystal structures and substantial structural distortions, which enable Rashba and Dresselhaus band splitting and persistent spin texture, confirmed by DFT calculations. Measurements of the resulting symmetry-dependent properties, such as second harmonic generation, switchable photovoltaic effect, ferroelectric polarization, and low temperature photoluminescence show switchable ferroelectric semiconducting properties. Furthermore, these results introduce a general chemical design strategy toward polar symmetry by exploiting the complex interplay between asymmetric spacer cations and A-site cations in quasi-2D (n > 1) halide perovskites to realize new classes of multifunctional materials for future spin–orbitronic applications.

Cations↗

DOE OSTI2020

Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics. 1. Theory, Algorithm, and Performance

By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal density functional theory (DFT) and thereby furnish a more accurate and reliable description of the underlying electronic structure in systems throughout biology, chemistry, physics, and materials science. However, the high computational cost associated with the evaluation of all required EXX quantities has limited the applicability of hybrid DFT in the treatment of large molecules and complex condensed-phase materials. To overcome this limitation, we describe a linear-scaling approach that utilizes a local representation of the occupied orbitals (e.g., maximally localized Wannier functions (MLWFs)) to exploit the sparsity in the real-space evaluation of the quantum mechanical exchange interaction in finite-gap systems. In this work, we present a detailed description of the theoretical and algorithmic advances required to perform MLWF-based ab initio molecular dynamics (AIMD) simulations of large-scale condensed-phase systems of interest at the hybrid DFT level. We focus our theoretical discussion on the integration of this approach into the framework of Car–Parrinello AIMD, and highlight the central role played by the MLWF-product potential (i.e., the solution of Poisson’s equation for each corresponding MLWF-product density) in the evaluation of the EXX energy and wave function forces. We then provide a comprehensive description of the exx algorithm implemented in the open-source Quantum ESPRESSO program, which employs a hybrid MPI/OpenMP parallelization scheme to efficiently utilize the high-performance computing (HPC) resources available on current- and next-generation supercomputer architectures. Furthermore, this is followed by a critical assessment of the accuracy and parallel performance (e.g., strong and weak scaling) of this approach when AIMD simulations of liquid water are performed in the canonical (NVT) ensemble. With access to HPC resources, we demonstrate that exx enables hybrid DFT-based AIMD simulations of condensed-phase systems containing 500–1000 atoms (e.g., (H₂O)₂₅₆) with a wall time cost that is comparable to that of semilocal DFT. In doing so, exx takes us one step closer to routinely performing AIMD simulations of complex and large-scale condensed-phase systems for sufficiently long time scales at the hybrid DFT level of theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Viewing Optical Processes at the Nanoscale: Combining Scanning Tunneling Microscopy and Optical Spectroscopy

The combination of optical spectroscopy and scanning tunneling microscopy techniques has the potential to yield the unambiguous single-molecule level insight required to fully understand the complex structure–property relationship of emerging material systems. In this Perspective, we highlight the recent progress of single-molecule absorption detected by scanning tunneling microscopy (SMA-STM) to investigate light–matter interactions of supported (nano)particles, quantum dots, and molecular and thin films at the nanoscale. We further show how the SMA-STM technique has been recently extended to expand the classes of materials that can be investigated as well as recent design modifications that enable both time-resolved imaging and tomography. Finally, we hypothesize how these modifications can be further leveraged to advance our current knowledge of light–matter interactions and how the resulting nanoscale insight can be implemented for future material applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Nematicity and nematic fluctuations in iron-based superconductors

The reduction of rotational symmetry in a crystalline solid driven by an electronic mechanism is referred to as electronic nematicity. This phenomenon – initially thought to be rare – has by now been observed in an increasing number of systems. Here, the iron-based superconductors present an ideal material platform to study nematicity in crystalline solids. Their nematic transition is pronounced, it can be studied with a wide range of experimental techniques; it is easily tunable; and high-quality samples are widely available. As research on nematicity in iron-based materials is now in its second decade, it builds on tremendous progress in theoretical concepts and experimental techniques. Thus, a stage has been reached at which the nematic phase can be addressed with confidence in its full complexity, including momentum-, time- and material- dependence of the order parameter. Important open questions concern the mechanism by which nematicity affects superconducting pairing and normal-state properties, with a central role for the challenging issue of nematic quantum criticality.

Electronic properties and materials↗

DOE OSTI2022

Direct Visualization of Magnetic Correlations in Frustrated Spinel ZnFe 2 O 4

Magnetic materials with the spinel structure (A 2+ B 3+ 2 O 4 ) form the core of numerous magnetic devices, and ZnFe 2 O 4 constitutes a peculiar example where the nature of the magnetism is still unresolved. Susceptibility measurements revealed a cusp around T c = 13 K resembling an antiferromagnetic transition, despite the positive Curie–Weiss temperature determined to be Θ CW = 102.8(1) K. Bifurcation of field-cooled and zero-field-cooled data below T c in conjunction with a frequency dependence of the peak position and a non-zero imaginary component below T c shows it is in fact associated with a spin-glass transition. Highly structured magnetic diffuse neutron scattering from single crystals develops between 50 K and 25 K revealing the presence of magnetic disorder which is correlated in nature. Here, the 3D-mΔPDF method is used to visualize the local magnetic ordering preferences, and ferromagnetic nearest-neighbor and antiferromagnetic third nearest-neighbor correlations are shown to be dominant. Their temperature dependence is extraordinary with some flipping in sign and a strongly varying correlation length. The correlations can be explained by orbital interaction mechanisms for the magnetic pathways and a preferred spin cluster. This study demonstrates the power of the 3D-mΔPDF method in visualizing complex quantum phenomena thereby providing a way to obtain an atomic-scale understanding of magnetic frustration.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Luminescent and Magnetic Tb-MOF Flakes Deposited on Silicon

The synthesis of a terbium-based 2D metal–organic framework (MOF), of formula [Tb(MeCOO)(PhCOO) 2 ] (1), a crystalline material formed by neutral nanosheets held together by Van der Waals interactions, is presented. The material can be easily exfoliated by sonication and deposited onto different substrates. Uniform distributions of Tb-2D MOF flakes onto silicon were obtained by spin-coating. We report the luminescent and magnetic properties of the deposited flakes compared with those of the bulk. Complex 1 is luminescent in the visible and has a sizeable quantum yield of QY = 61% upon excitation at 280 nm. Photoluminescence measurements performed using a micro-Raman set up allowed us to characterize the luminescent spectra of individual flakes on silicon. Magnetization measurements of flakes-on-silicon with the applied magnetic field in-plane and out-of-plane display anisotropy. Ac susceptibility measurements show that 1 in bulk exhibits field-induced slow relaxation of the magnetization through two relaxation paths and the slowest one, with a relaxation time of τ lf ≈ 0.5 s, is assigned to a direct process mechanism. The reported exfoliation of lanthanide 2D-MOFs onto substrates is an attractive approach for the development of multifunctional materials and devices for different applications.

2D MOFs↗

DOE OSTI2024

Probing plexciton emission from 2D materials on gold nanotrenches

Probing strongly coupled quasiparticle excitations at their intrinsic length scales offers unique insights into their properties and facilitates the design of devices with novel functionalities. In this work, we investigate the formation and emission characteristics of plexcitons, arising from the interaction between surface plasmons in narrow gold nanotrenches and excitons in monolayer WSe 2 . We study this strong plasmon–exciton coupling in both the far-field and the near-field. Specifically, we observe a Rabi splitting in the far-field reflection spectra of about 80 meV under ambient conditions, consistent with our theoretical modeling. Using a custom-designed near-field probe, we find that plexciton emission originates predominantly from the lower-frequency branch, which we can directly probe and map its local field distribution. We precisely determine the plexciton's spatial extension, similar to the trench width, with nanometric precision by collecting spectra at controlled probe locations. Our work opens exciting prospects for nanoscale mapping and engineering of plexcitons in complex nanostructures with potential applications in nanophotonic devices, optoelectronics, and quantum electrodynamics in nanoscale cavities.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Nanoscale interplay of native point defects near Sr-deficient SrxTiO 3 /SrTiO 3 interfaces

SrTiO 3 has many applications involving interfaces with other materials, but defects that affect the properties of SrTiO 3 films can also play a significant role at its heterointerfaces and even at junctions with nonstoichiometric SrTiO 3 . Depth-resolved cathodoluminescence spectroscopy (DRCLS) combined with systematic cation Sr content reduction in SrxTiO 3 ultrathin films grown on SrTiO 3 showed an interplay of native point defects and electronic structure within the Sr-deficient film and how interplay extends tens of nanometers into the substrate below. Understanding how defects form and affect interface electronic structure during epitaxial growth is central to improving complex oxide devices. Controlling the balance of oxygen vacancy defects with strontium vacancies and other acceptor-like defects can improve control over free carrier densities. Likewise, control over nanoscale defect charge distributions can advance new device features such as two-dimensional hole gases and the performance of existing devices such as ferroelectric tunnel junctions. This study shows how DRCLS directly measures the relative densities and spatial distributions of multiple native defects within and extending away from nanoscale SrxTiO 3 /SrTiO 3 junctions and how their interplay varies with controlled epitaxial growth. In turn, these growth-dependent defect variations can help guide SrTiO 3 epitaxial growth with other complex oxides.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multiscale Ecosystem for solving Maxwell-Schrodinger equations of open quantum systems (OpenMS)

Light-matter interactions play an important role in many branches of physics, chemistry, energy, and materials science. In the strong coupling regime, light-matter interactions are able to tune the materials properties via the formation of new quasiparticles, such as plasmons and polaritons. However, theoretical and numerical modeling of the light-matter interaction-mediated processes remain a big challenge because light-matter interactions are fundamentally multiscale and multiphysics problems involving multiple interactions between electrons, nuclei, and photons at different time/length scales. In the current community, light-matter interactions were treated at different levels of theoretical complexity in quantum chemistry and quantum optics. In quantum optics or quantum photonics, the matter is usually simplified as a few-level system, and the light is treated quantum-mechanically. On the other hand, quantum chemistry explores first-principles methods, including both single-particle and many-body-based techniques, to describe the electronic properties of matter in detail. However, light is usually prescribed as a classical electromagnetic field, and the light-matter interaction is taken into account as an external potential via classical approximations. This software is designed to fill current modeling shortcomings by delivering a first-ever scalable multiscale platform for simulating light-matter interactions in realistic electromagnetic environments. The software solves Maxwell and Schrodinger equations self-consistent on the heterogeneous platforms. It implements HF/DFT, TDDFT, and coupled-cluster counterparts for light-matter interactions and adopts modular programming to offload massively parallel algorithms on a large number of CPU/GPUs.

Zhang, Yu↗

DOE OSTI2022

In Situ Probing and Investigation of the Crystal Growth of Highly Correlated Intermetallics

The proposal seeks to investigate the synthetic process of a new class of highly correlated intermetallics Ce n+1 Co n Ge 3n+1 (n = 1 – 6). This family of compounds is attractive as members of the series consist of subunits found in quantum materials. With our efforts to grow single crystals, we will address the fundamental questions of 1) how do crystals that are built from subunits form during crystallization and 2) what determines how these subunits arrange. We hypothesize that the complex intergrowth structures of Ce n+1 Co n Ge 3n+1 form upon cooling by the assembly of subunit-like clusters that exist prior to crystallization in the melt. The second hypothesis is layer-by-layer growth. The third hypothesis is an alternative growth mechanism, such as the conversion of metastable structure into a different structure. By understanding the assembly of subunits, strategies to affect process could emerge, which will ultimately lead to controlled heterostructural stacking of various subunits in the growth of bulk materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Structural complexity in ramp-compressed sodium to 480 GPa

Abstract The properties of all materials at one atmosphere of pressure are controlled by the configurations of their valence electrons. At extreme pressures, neighboring atoms approach so close that core-electron orbitals overlap, and theory predicts the emergence of unusual quantum behavior. We ramp-compress monovalent elemental sodium, a prototypical metal at ambient conditions, to nearly 500 GPa (5 million atmospheres). The 7-fold increase of density brings the interatomic distance to 1.74 Å well within the initial 2.03 Å of the Na + ionic diameter, and squeezes the valence electrons into the interstitial voids suggesting the formation of an electride phase. The laser-driven compression results in pressure-driven melting and recrystallization in a billionth of a second. In situ x-ray diffraction reveals a series of unexpected phase transitions upon recrystallization, and optical reflectivity measurements show a precipitous decrease throughout the liquid and solid phases, where the liquid is predicted to have electronic localization. These data reveal the presence of a rich, temperature-driven polymorphism where core electron overlap is thought to stabilize the formation of peculiar electride states.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Controlled Organization of Inorganic Materials Using Biological Molecules for Activating Therapeutic Functionalities

Precise control over the assembly of biocompatible three-dimensional (3D) nanostructures would allow for programmed interactions within the cellular environment. Nucleic acids can be used as programmable crosslinkers to direct the assembly of quantum dots (QDs) and tuned to demonstrate different interparticle binding strategies. In this work, morphologies of self-assembled QDs are evaluated via gel electrophoresis, transmission electron microscopy, small-angle X-ray scattering, and dissipative particle dynamics simulations, with all results being in good agreement. The controlled assembly of 3D QD organizations is demonstrated in cells via the colocalized emission of multiple assembled QDs, and their immunorecognition is assessed via enzyme-linked immunosorbent assays. RNA interference inducers are also embedded into the interparticle binding strategy to be released in human cells only upon QD assembly, which is demonstrated by specific gene silencing. The programmability and intracellular activity of QD assemblies offer a strategy for nucleic acids to imbue the structure and therapeutic function into the formation of complex networks of nanostructures, while the photoluminescent properties of the material allow for optical tracking in cells in vitro.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Hybrid MEMS-CMOS ion traps for NISQ computing

Surging interest in engineering quantum computers has stimulated significant and focused research on technologies needed to make them manufacturable and scalable. In the ion trap realm this has led to a transition from bulk three-dimensional macro-scale traps to chip-based ion traps and included important demonstrations of passive and active electronics, waveguides, detectors, and other integrated components. At the same time as these technologies are being developed the system sizes are demanding more ions to run noisy intermediate scale quantum (NISQ) algorithms, growing from around ten ions today to potentially a hundred or more in the near future. To realize the size and features needed for this growth, the geometric and material design space of microfabricated ion traps must expand. In this paper we describe present limitations and the approaches needed to overcome them, including how geometric complexity drives the number of metal levels, why routing congestion affects the size and location of shunting capacitors, and how RF power dissipation can limit the size of the trap array. Finally, we also give recommendations for future research needed to accommodate the demands of NISQ scale ion traps that are integrated with additional technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Spectroscopic Probe Molecule Selection Using Quantum Theory, First-Principles Calculations, and Machine Learning

Probe molecule vibrational spectra have a long history of being used to characterize materials including metals, oxides, metal-organic frameworks, and even human proteins. Furthermore, recent advances in machine learning have enabled computationally generated spectra to aid in detailed characterization of complex surfaces with probe molecules. Despite widespread use of probe molecules, the science of probe molecule selection is underdeveloped. Here, we develop physical concepts, including orbital interaction energy and the energy overlap integral, to explain and predict the ability of probe molecules to discriminate structural descriptors. We resolve the crystal orbital overlap population (COOP) to specific molecular orbitals and quantify their bonding character, which directly influences vibrational frequencies. Using only a single adsorbate calculation from density function theory (DFT), we compute the interaction energy of individual adsorbate molecular orbitals with adsorption site atomic orbitals across many different sites. Combining the molecular orbital resolved COOP and changes in orbital interaction energy enables probe molecule selection for improved discrimination of various sites. We demonstrate these concepts by comparing the predicted effectiveness of carbon monoxide (CO), nitric oxide (NO), and ethylene (C 2 H 4 ) to probe Pt adsorption sites. Finally, using a previously developed machine learning framework, we show that models trained on hundreds of thousand C 2 H 4 spectra, computed from DFT, which regress surface binding-type and generalized coordination number (GCN), outperform those trained using CO and NO spectra. Lastly, a python package, pDOS_overlap, for implementing the electron density based analysis on any combination of adsorbates and materials, is also made available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

Exploring the scaling limitations of the variational quantum eigensolver with the bond dissociation of hydride diatomic molecules

Abstract Materials simulations involving strongly correlated electrons pose fundamental challenges to state‐of‐the‐art electronic structure methods but are hypothesized to be the ideal use case for quantum computing algorithms. To date, no quantum computer has simulated a molecule of a size and complexity relevant to real‐world applications, despite the fact that the variational quantum eigensolver (VQE) algorithm can predict chemically accurate total energies. Nevertheless, because of the many applications of moderately sized, strongly correlated systems, such as molecular catalysts, the successful use of the VQE stands as an important waypoint in the advancement toward useful chemical modeling on near‐term quantum processors. In this paper, we take a significant step in this direction. We lay out the steps, write, and run parallel code for an (emulated) quantum computer to compute the bond dissociation curves of the TiH, LiH, NaH, and KH diatomic hydride molecules using the VQE. TiH was chosen as a relatively simple chemical system that incorporates d orbitals and strong electron correlation. Because current VQE implementations on existing quantum hardware are limited by qubit error rates, the number of qubits available, and the allowable gate depth, recent studies using it have focused on chemical systems involving s and p block elements. Through VQE + UCCSD calculations of TiH, we evaluate the near‐term feasibility of modeling a molecule with d‐orbitals on real quantum hardware. We demonstrate that the inclusion of d‐orbitals and the use of the UCCSD ansatz, which are both necessary to capture the correct TiH physics, dramatically increase the cost of this problem. We estimate the approximate error rates necessary to model TiH on current quantum computing hardware using VQE + UCCSD and show them to likely be prohibitive until significant improvements in hardware and error correction algorithms are available.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗