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DOE OSTI2024

PhILMs: Collaboratory on Mathematics and Physics-Informed Learning Machines for Multiscale and Multiphysics Problems

The landscape of computational science and engineering is continually evolving, with the challenge of high-dimensional regression problems standing as a significant hurdle in numerous scientific endeavors. Addressing this challenge, our research, funded by this award, has led to the development of an innovative computational framework known as Probabilistic Partition of Unity Networks (PPOU-Nets). This initiative represents a collaborative effort to harness the potential of mathematics and physics-informed machine learning in tackling multiscale and multiphysics problems prevalent in high-dimensional spaces. Through this work, we have proposed a novel methodology that seamlessly integrates adaptive dimensionality reduction and a mixture of experts model, thereby facilitating a more efficient and accurate approximation of complex functions. This research effort has not only advanced the state of computational science but also opened new avenues for exploration in quantum computing and beyond. This report outlines the motivation, methodology, key findings, and implications of our work, underscoring our contributions to the broader scientific community and the potential pathways for future research.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Universal Superconductivity in FeTe and All‐Iron‐Based Ferromagnetic Superconductor Heterostructures

Abstract Ferromagnetism (FM) and superconductivity (SC) are two of the most famous macroscopic quantum phenomena. However, nature normally does not allow SC and FM to coexist without significant degradation. Here, the first fully iron‐based SC/FM heterostructures, composed of Fe(Te,Se) and Fe 3 GeTe 2 , are introduced, and it is shown that this system exhibits both strong FM and high‐temperature SC with an atomically sharp interface. From this study, it is also discovered that minute level of various cationic dopants can drive otherwise non‐superconducting FeTe films into a SC state. This suggests that the ground state of FeTe is so close to the SC state that it can be driven in and out of the SC state with various other perturbations. Altogether, this shows that Fe‐Te‐based heterostructures provide a unique opportunity to manipulate magnetism, superconductivity, and topological physics, paving the way toward new superconducting technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Large moiré superstructure of stacked incommensurate charge density waves

This article reports how two different charge density waves with slightly different wavevectors can exist in the same crystal and create moiré superstructure. Advances in heterostructure fabrication have opened new frontiers in moiré physics. Here we extend moiré engineering from artificially assembled thin flakes with mismatched lattice parameters to materials that host incommensurate orders, presenting a long-period moiré superlattice in a layered charge-density-wave compound, EuTe 4 . Using high-momentum-resolution X-ray diffraction, we found two coexisting incommensurate charge density waves with slightly mismatched in-plane wavevectors. The interaction between these two charge density waves leads to joint commensuration with the lattice and a moiré superstructure with a period of ~13.6 nm, offering key insights into the unique properties of EuTe4, such as the temperature-invariant incommensurate wavevectors and unconventional in-gap states. Owing to interlayer phase shifts, the moiré superstructure exhibits a clear thermal hysteresis, accounting for the large hysteresis in electrical resistivity and numerous metastable states. Our findings open new directions for moiré engineering based on incommensurate lattices and highlight the important role of interlayer ordering in stacked structures.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Surface Chemistry and Reactions for Bimetallic Au Catalysis (Final Report)

Gold-based catalysts offer tremendous opportunities for developing new technologies for selective oxidation reactions that directly use gas-phase oxygen as a benign reagent and produce either no byproducts or water as the only byproduct. These processes are urgently needed and critical for achieving the sustainable production of chemicals. While recent studies demonstrate that the catalytic properties of gold can be significantly enhanced by addition of other metals, the development of these catalysts with sufficient performance for commercialization is hindered by a lack of fundamental understanding of how the catalytic properties of gold can be controlled and tailored. This project addressed this need by synergistically combining experimental and computational studies to establish fundamental composition-structure-activity relationships for gold-based catalysts for several industrially important reactions. Results from surface spectroscopy, atomic imaging and thermal desorption experiments were combined with molecular models and reaction mechanisms from quantum chemical calculations to link observable reaction rates to the structure and composition of surfaces at the molecular level. The results were used to develop improved catalyst formulations and identify optimized reaction conditions for gold-based catalysts. For broader impacts, the developed methodology was extended to other metallic catalysts, such as silver, platinum-molybdenum and nickel-tin, for improved catalytic efficiency and sustainable production of specialty and commodity chemicals.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Quantum annealing-assisted lattice optimization

High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Optomechanical self-organization in a mesoscopic atom array

Increasing the number of particles in a system often leads to qualitative changes in its properties, such as breaking of symmetries and the appearance of phase transitions. This renders a macroscopic system fundamentally different from its individual microscopic constituents. Lying between these extremes, mesoscopic systems exhibit microscopic fluctuations that influence behaviour on longer length scales, leading to critical phenomena and dynamics. Therefore, tracing the properties of well-controlled mesoscopic systems can help bridge the gap between an exact description of few-body microscopic systems and the emergent description of many-body systems. Here we explore the mesoscopic signatures of an optomechanical self-organization phase transition using arrays of cold atoms inside an optical cavity. By precisely engineering atom–cavity interactions, we reveal how critical behaviour depends on the atom number, identify characteristic dynamical behaviours in the self-organized regime and observe a finite optomechanical susceptibility at the critical point. These findings advance our understanding of particle-number- and time-resolved properties of phase transitions in mesoscopic systems.

Atomic and molecular interactions with photons↗

DOE OSTI2022

Ramsey envelope modulation in NV diamond magnetometry

Nitrogen-vacancy (NV) spin ensembles in diamond provide an advanced magnetic sensing platform, with applications in both the physical and life sciences. The development of isotopically engineered 15 NV diamond offers advantages over naturally occurring 14 NV for magnetometry, due to its simpler hyperfine structure. However, for sensing modalities requiring a bias magnetic field not aligned with the sensing NV axis, the absence of a quadrupole moment in the 15 N nuclear spin leads to pronounced envelope modulation effects in time-dependent measurements of 15 NV spin evolution. While such behavior in spin echo experiments are well studied, analogous effects in Ramsey measurements and the implications for magnetometry remain underexplored. Here, we derive the modulated 15 NV Ramsey response to a misaligned bias field, using a simple vector description of the effective magnetic field on the nuclear spin. The predicted modulation properties are then compared to experimental results, revealing significant magnetic sensitivity loss if unaddressed. We demonstrate that double-quantum coherences of the NV S=1 electronic spin states dramatically suppress these envelope modulations, while additionally proving resilient to other parasitic effects such as strain heterogeneity and temperature shifts.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Emergent interaction-induced topology in Bose-Hubbard ladders

We investigate the quantum many-body dynamics of bosonic atoms hopping in a two-leg ladder with strong on-site contact interactions. We observe that when the atoms are prepared in a staggered pattern with pairs of atoms on every other rung, singlon defects, i.e., rungs with only one atom, can localize due to an emergent topological model, even though the underlying model in the absence of interactions admits only topologically trivial states. This emergent topological localization results from the formation of a zero-energy edge mode in an effective lattice formed by two adjacent chains with alternating strong and weak hoping links (Su-Schrieffer-Heeger chains) and opposite staggering which interface at the defect position. Our findings open the opportunity to dynamically generate nontrivial topological behaviors without the need for complex Hamiltonian engineering. Published by the American Physical Society 2025

Wellnitz, David (ORCID:0000000349788938)↗

DOE OSTI2024

Metal-to-insulator transition in oxide semimetals by anion doping

Dirac semimetals exhibiting nontrivial topological characteristics stand as exemplary parent compounds, offering great promise for the realization of novel electronic states. These materials also serve as a versatile platform for systematically investigating topological quantum phase transitions. In this study, we present compelling evidence of profound structural and transport phase shifts in a recently uncovered oxide Dirac semimetal, SrNbO3, achieved through effective in situ anion doping. Notably, a remarkable increase in resistivity of more than three orders of magnitude at room temperature is observed upon nitrogen-doping. The extent of electronic modulation in SrNbO3 is strongly correlated with the misfit strain, underscoring its phase instability to both chemical doping and crystallographic symmetry variations. Using first-principles calculations, we discern that elevating the level of nitrogen doping induces an upward shift in the conductive bands of SrNbO3-?N?. Consequently, a transition from a metallic state to an insulating state becomes apparent as the nitrogen concentration reaches a threshold of 1/3. This investigation sheds light on the potential of anion engineering in oxide Dirac semimetals, offering pathways for manipulating their physical properties. These insights hold promise for future applications that harness these materials for tailored functionalities.

Hong, Haitao↗

DOE OSTI2020

DNA-assembled superconducting 3D nanoscale architectures

Studies of nanoscale superconducting structures have revealed various physical phenomena and led to the development of a wide range of applications. Most of these studies concentrated on one- and two-dimensional structures due to the lack of approaches for creation of fully engineered three-dimensional (3D) nanostructures. Here, we present a ‘bottom-up’ method to create 3D superconducting nanostructures with prescribed multiscale organization using DNA-based self-assembly methods. We assemble 3D DNA superlattices from octahedral DNA frames with incorporated nanoparticles, through connecting frames at their vertices, which result in cubic superlattices with a 48 nm unit cell. The superconductive superlattice is formed by converting a DNA superlattice first into highly-structured 3D silica scaffold, to turn it from a soft and liquid-environment dependent macromolecular construction into a solid structure, following by its coating with superconducting niobium (Nb). Through low-temperature electrical characterization we demonstrate that this process creates 3D arrays of Josephson junctions. This approach may be utilized in development of a variety of applications such as 3D Superconducting Quantum interference Devices (SQUIDs) for measurement of the magnetic field vector, highly sensitive Superconducting Quantum Interference Filters (SQIFs), and parametric amplifiers for quantum information systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Method and apparatus of surface-incident, plasmon-enhanced multiple quantum well modulators and optical coupling thereon

An optical interconnect system has first and second waveguides each with wedge-shaped cross-section at a first end, disposed over an optical modulator. The optical modulator is a surface-plasmon multi quantum well (SP-MQW) modulator, the first waveguide an input waveguide and the second waveguide configured an output waveguide. In embodiments the SP-MQW modulator has multiple semiconductor layers disposed atop a lower metal layer between 10 and 300 nanometers thick and configured such that incident light is reflected at the lower metal layer unless a voltage is applied to the semiconductor layers, when incident light is coupled into a surface plasmon mode in the lower metal layer.

42 ENGINEERING↗

NASA NTRS2011

Volumetric 3D Display System with Static Screen

Current display technology has relied on flat, 2D screens that cannot truly convey the third dimension of visual information: depth. In contrast to conventional visualization that is primarily based on 2D flat screens, the volumetric 3D display possesses a true 3D display volume, and places physically each 3D voxel in displayed 3D images at the true 3D (x,y,z) spatial position. Each voxel, analogous to a pixel in a 2D image, emits light from that position to form a real 3D image in the eyes of the viewers. Such true volumetric 3D display technology provides both physiological (accommodation, convergence, binocular disparity, and motion parallax) and psychological (image size, linear perspective, shading, brightness, etc.) depth cues to human visual systems to help in the perception of 3D objects. In a volumetric 3D display, viewers can watch the displayed 3D images from a completely 360 view without using any special eyewear. The volumetric 3D display techniques may lead to a quantum leap in information display technology and can dramatically change the ways humans interact with computers, which can lead to significant improvements in the efficiency of learning and knowledge management processes. Within a block of glass, a large amount of tiny dots of voxels are created by using a recently available machining technique called laser subsurface engraving (LSE). The LSE is able to produce tiny physical crack points (as small as 0.05 mm in diameter) at any (x,y,z) location within the cube of transparent material. The crack dots, when illuminated by a light source, scatter the light around and form visible voxels within the 3D volume. The locations of these tiny voxels are strategically determined such that each can be illuminated by a light ray from a high-resolution digital mirror device (DMD) light engine. The distribution of these voxels occupies the full display volume within the static 3D glass screen. This design eliminates any moving screen seen in previous approaches, so there is no image jitter, and has an inherent parallel mechanism for 3D voxel addressing. High spatial resolution is possible with a full color display being easy to implement. The system is low-cost and low-maintenance.

Geng, Jason↗

DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Ambipolar Transport in Polycrystalline GeSn Transistors for Complementary Metal-Oxide-Semiconductor Applications

Group-IV alloy GeSn is a promising material for electronic and optoelectronic applications due to its compatibility with both Si substrates and established Si fabrication processes. This study focuses on polycrystalline GeSn (10% Sn), which offers a cost-effective, large-area, and versatile alternative to epitaxial GeSn. We demonstrate ambipolar transport behavior in polycrystalline GeSn thin film transistors, achieving electron and hole field-effect mobilities reaching up to 0.05 cm 2 /Vs and 2.05 cm 2 /Vs, respectively. Through temperature-dependent analysis, we elucidate the underlying mechanism of this phenomenon, which we attribute to quantum tunneling between the Schottky barrier contact and the channel, as well as potential barriers between the grain boundaries of this polycrystalline film, thereby advancing the understanding of polycrystalline GeSn's electrical properties. Furthermore, this work highlights the potential of ambipolar transport as a technique to employ towards the development of GeSn complementary metal-oxide-semiconductor field-effect transistors, promising to simplify and reduce the cost of GeSn manufacturing processes for edge computing and sensing applications.

42 ENGINEERING↗

DOE OSTI2021

Spectroscopic Signatures of MQ-Resins in Silicone Elastomers

Polysiloxane elastomers have a large application space due to their versatile cross-linking chemistry and highly tunable physical and mechanical properties. One approach for improving the mechanical integrity of commercial polysiloxane “silicone” elastomers while maintaining their optical transparency is the addition of small, silicone-resin molecules to the network. However, both the poly(dimethylsiloxane) (PDMS) network and the silicone-resin particles have an amorphous structure and complex chemistry, which makes the characterization of their structural properties and segmental network dynamics difficult. Here, we report the synthesis and characterization of a series of model silicone networks modified with a specific class of silicone-resin known as MQ-resin using Raman and advanced nuclear magnetic resonance (NMR) spectroscopy methods. Raman spectroscopy was successfully used to quantify the contribution of the MQ-resin to the network, to determine the type of MQ-resin present in the network, and to investigate the completeness of the network cross-linking reaction. Furthermore, solid-state and 1H double-quantum (DQ) NMR spectroscopies were used not only as a detection method for the MQ-resins but also to quantify changes in the segmental dynamics of the network as a function of MQ-resin concentration. The combination of Raman and NMR spectroscopies describes a series of samples where the MQ-resin particles and PDMS chains maintain their independent segmental dynamics up to high concentrations of MQ-resins (40–50% MQ), where the physical properties of the resin dominate the physical properties of the overall network. The results from our spectroscopic analyses are consistent with the results from macroscopic characterization techniques such as solvent uptake and mechanical testing. The spectroscopic insights into the structure–property relationships of PDMS-MQ composites presented in this study are a valuable tool not only for the synthesis and reverse engineering of future generations of commercial silicone elastomers but also for understanding the mechanisms of aging and degradation over the material lifetime.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Hot-Carrier Extraction in Nanowire-Nanoantenna Photovoltaic Devices

Nanowires bring new possibilities to the field of hot-carrier photovoltaics by providing flexibility in combining materials for band engineering and using nanophotonic effects to control light absorption. Previously, an open-circuit voltage beyond the Shockley–Queisser limit was demonstrated in hot-carrier devices based on InAs–InP–InAs nanowire heterostructures. However, in these first experiments, the location of light absorption, and therefore the precise mechanism of hot-carrier extraction, was uncontrolled. In this study, we combine plasmonic nanoantennas with InAs–InP–InAs nanowire devices to enhance light absorption within a subwavelength region near an InP energy barrier that serves as an energy filter. From photon-energy- and irradiance-dependent photocurrent and photovoltage measurements, we find that photocurrent generation is dominated by internal photoemission of nonthermalized hot electrons when the photoexcited electron energy is above the barrier and by photothermionic emission when the energy is below the barrier. We estimate that an internal quantum efficiency up to 0.5–1.2% is achieved. Insights from this study provide guidelines to improve internal quantum efficiencies based on nanowire heterostructures.

14 SOLAR ENERGY↗

DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Transmission electron microscopy with in-situ ion irradiation: Facilities and community

Whilst there is a clear scientific and technological need for the technical capabilities of transmission electron microscopes with in-situ ion irradiation, it also requires a collaborative community of international researchers to support such facilities in successfully meeting this demand. Instruments of this type serve to provide fundamental understanding of the mechanisms which drive changes in materials important to nuclear fission and fusion energy, the semiconductor industry, quantum information systems, space travel, astronomy, geology and many more applications. As these areas continue to evolve and the instrumentation possibilities expand, the capacity of in-situ ion irradiation facilities must also develop hand-in-hand with the user community to deliver an ever-greater diversity of high-fidelity extreme-environment experimentation. Future directions for the field, such as miniaturization from MEMS/microfluidic devices and advanced controls with ML-based analysis, continuously emerge to advance both the hardware and software which support the coupling of TEMs with ion beams. This review sets out to provide up-to-date insights into the community and advancement of current, and development of future, facilities which have the potential to further unlock access to the nanoscale exploration of coupled extreme environments crucial to many of the important science and engineering challenges we face today.

In-situ irradiation↗