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DOE OSTI2024

Machine-assisted discovery of integrable symplectic mappings

Integrable systems possess a hidden symmetry associated with the existence of conserved quantities known as integrals of motion. These systems play an important role in understanding general dynamics in accelerators and have potential for future designs. This work will cover two automated methods for finding integrable symplectic maps of the plane. The first algorithm is based on the observation that the evolution of an integrable system in phase space is confined to a lower-dimensional submanifold of a specific type. The second algorithm relies on an analysis of dynamical variables. Both methods rediscover some of the famous McMillan-Suris integrable mappings and ultra-discrete Painlev\'e equations. Over 100 new integrable families are presented and analyzed, some of which are isolated in the space of parameters, while others are families with one parameter (or the ratio of parameters) being either continuous or discrete. In addition, the newly discovered maps are related to a general 2D symplectic map through the use of discrete perturbation theory. A method is proposed for constructing smooth near-integrable dynamical systems based on mappings with polygon invariants.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Machine-Assisted Discovery of Integrable Symplectic Mappings

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Comparative performance assessment of air-source and ground-source heat pumps using CO₂ and R-410A with water well integration: A simulation study

This study investigates the performance and feasibility of heat pump systems for residential space heating in cold climates, with a particular focus on ground source heat pumps (GSHPs) with water wells. Four configurations are modeled and compared, a CO 2 air-source heat pump (ASHP), an R-410A ASHP, a CO 2 GSHP with water well integration, and an R-410A ground-source heat pump with water well integration. System simulations are conducted at both equipment and whole-building levels, followed by a nationwide analysis across ten representative cities using EnergyPlus. Results show that water-well-coupled GSHPs maintain approximately 87 % of their efficiency and 85.5 % of heating capacity as ambient temperature drops from 47 °F/8.3 °C to −15 °F/-26.1 °C, whereas ASHPs retain only 55 % efficiency and 44.5 % capacity. R-410A systems achieve higher efficiency, while CO 2 systems provide significant environmental advantages, including reduced risks of groundwater contamination from refrigerant leakage. On average, the CO 2 GSHPs deliver 35–45 % heating energy savings relative to ASHPs and demonstrate strong potential as a replacement for gas furnaces in cold climates. This work represents a systematic comparative assessment of CO 2 and R-410A air-source and ground-source heat pumps with water well integration. In conclusion, the findings highlight the technical viability, environmental benefits and deployment potential of CO 2 GSHPs, offering a pathway toward efficient and sustainable residential heating solutions in diverse U.S. climates.

CO 2 GSHP↗

DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Parametric optimization of PCM-enhanced underground thermal energy storage for buildings in moderate cold climates

This study presents a novel underground thermal energy storage (UTES) system designed for space heating in buildings located in moderate cold climates. The proposed UTES features a borehole with a depth of 25 ft. (7.62 m) and a diameter of 3 ft. (0.91 m), containing two helical pipe loops-one for discharge fluid and another for recharge fluid-and a thermally enhanced phase change material (PCM) layer that provides high energy storage capacity. The system requires daily thermal recharging using a low-grade heat source for a few hours and delivers continuous heating at a nearly constant discharge rate without significant performance degradation over a 24-h period. This study demonstrates that the optimized UTES, when recharged with hot fluid at an inlet temperature of 40 degrees C for 6 h daily, can provide a continuous heat discharge rate of approximately 2.9 kW for 24 h or 3.4 kW if operated under a shorter discharge period of 15 h (17:00-08:00). With a round-trip efficiency of 72-97%, the proposed UTES offers an efficient and reliable solution for short- and long-duration thermal energy storage technology, making it a promising technology for cold climates.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Multi‐Sensor Trajectory Reconstruction of the 24 April 2025 Alaska Fireball and Implications for Planetary Defense

On 24 April 2025 at 18:30:57 UTC, a bright daytime fireball over Southcentral Alaska was detected by 37 seismic stations, 16 single infrasound sensors, and four infrasound arrays, yielding 30 ballistic and multiple fragmentation arrivals. Here, the unprecedented density of seismoacoustic coverage enabled detailed reconstruction of the event using acoustic signals, with fragmentation source locations further guiding the identification of Doppler weather radar signatures of a meteorite fall. Incorporation of a radar-derived terminal point yielded a final trajectory solution, which agreed closely with an independent optical trajectory solution from video analysis. The reconstructed entry parameters from seismoacoustic analysis indicate a velocity of 25.3 km/s, an entry angle of 19°, and an energy release of ∼38 t TNT equivalent. Assuming a chondritic composition, the pre-entry object diameter was ∼0.7 m. Using orbital parameters from the optical solution, we estimate meteoroid composition as most likely an L-type ordinary chondrite. The event occurred in the sub-Arctic, where space-based optical systems face challenges in detection, demonstrating the critical role of dense ground-based seismoacoustic networks in characterizing high-latitude atmospheric entries. This uniquely well-recorded event demonstrates the capability of dense seismoacoustic networks to constrain bolide trajectories, energetics, and fragmentation, with radar and optical data providing critical confirmation and complementary perspectives. These results bridge the methodological gap between planetary-defense monitoring of natural impactors and space-traffic analyses of artificial reentries, illustrating how multi-sensor integration can deliver calibration-grade trajectories even for unpredicted events.

Fireball↗

DOE OSTI2026

Profusion of symmetry-protected qubits from stable ergodicity breaking

We show how combining a discrete symmetry with topological Hilbert space fragmentation can give rise to exponentially many topologically stable qubits protected by a single discrete symmetry. We illustrate this explicitly with the example of the CZ𝑝 model, where the encoded qubits are prethermally stable to arbitrary symmetry-respecting perturbations for parametrically long times, substantially enhancing the robustness of a recently proposed construction based on nontopological fragmentation. In this model, the encoded qubits naturally come in pairs for which a universal set of transversal logical gates can be performed, ruling out (by the Eastin-Knill theorem) the possibility of using them for quantum error correction. We also comment on the combination of symmetry enrichment and topological fragmentation more generally, and the implications for use of systems exhibiting Hilbert space fragmentation as quantum memories.

kinetically constrained models↗

DOE OSTI2024

Low Power, Radiation Resilient Synchronous Edge Processing for Remote Monitoring

Next-generation space remote sensing systems may be equipped with imaging arrays that sense data at a rate that outstrips the processing capability of any computing hardware that can operate within a satellite’s power budget. This project developed novel convolutional and recurrent neural networks to detect and estimate point-like events amid clutter, and investigated their efficient and accurate implementation on analog in-memory computing systems that are 10-1000× more energy-efficient than digital processors. This project leveraged two memory devices at different levels of technological maturity: a large-scale analog computing prototype using commercial SONOS charge-trap memory, and electrochemical memory (ECRAM) with intrinsic radiation hardness. We experimentally demonstrated end-to-end analog processing of our neural networks on SONOS and characterized the radiation response of both SONOS and ECRAM. We advanced the state-of-the-art in ECRAM precision and reliability, and developed co-design methods to enable accurate long-term operation of SONOS analog accelerators in space radiation environments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems

Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Optical Control of Adaptive Nanoscale Domain Networks

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO 3 ) n /(SrTiO 3 ) n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light-programable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Improving Miscibility of Polymer Donor and Polymer Acceptor by Reducing Chain Entanglement for Realizing 18.64 % Efficiency All Polymer Solar Cells

All-polymer solar cells have experienced rapid development in recent years by the emergence of polymerized small molecular acceptors (PSMAs). However, the strong chain entanglements of polymer donors (P D s) and polymer acceptors (P A s) decrease the miscibility of the resulting polymer mixtures, making it challenging to optimize the blend morphology. Herein, we designed three P A s, namely PBTPICm-BDD, PBTPICγ-BDD and PBTPICF-BDD, by smartly using a BDD unit as the polymerized unit to copolymerize with different Y-typed non-fullerene small molecular acceptors (NF-SMAs), thus achieving a certain degree of distortion and giving the polymer system enough internal space to reduce the entanglements of the polymer chains. Such effects increase the chances of the P D being interspersed into the acceptor material, which improve the solubility between the P D and P A . The PBTPICγ-BDD and PBTPICF-BDD displayed better miscibility with PBQx-TCl, leading to a well optimized morphology. As a result, high power conversion efficiencies (PCEs) of 17.50 % and 17.17 % were achieved for PBQx-TCl : PBTPICγ-BDD and PBQx-TCl : PBTPICF-BDD devices, respectively. In conclusion, with the addition of PYFT-o as the third component into PBQx-TCl : PBTPICγ-BDD blend to further extend the absorption spectral coverage and finely tune microstructures of the blend morphology, a remarkable PCE of 18.64 % was realized finally.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Parton distribution functions from scalar light-front parton gas model

Here, we propose an application of a microcanonical ensemble with light-front kinematics to model the phase-space distribution of relativistic constituents of a bound state. These constituents denoted by partons are treated as classical spin-zero particles confined inside the bound state with inter-parton collisions as their only interaction. The microcanonical molecular dynamics ensemble is applied to obtain the phase-space distribution of such a thermodynamic system. We sample this phase-space distribution using Monte Carlo algorithms to obtain the parton distribution functions (PDFs) in scenarios with 3, 4, and 5 identical partons. In addition PDFs when a selected number of massless partons are mixed with 3 massive partons are also presented.

Microcanonical ensemble↗

DOE OSTI2025

Crystal Growth of Quaternary Rare Earth Selenosilicates by Using the Flux-Assisted Boron Chalcogen Mixture Method: Investigation of Their Magnetic and Optical Properties

A series of rare earth magnesium selenosilicates, RE3Mg 0.5 SiSe 7 (RE = Ce, Pr, Nd, Sm, Gd, Tb, Dy) were obtained as single crystal using the flux assisted boron chalcogen mixture (BCM) method. The structures of the crystals were determined by single-crystal X-ray diffraction. The RE3Mg 0.5 SiSe 7 series crystallizes in the hexagonal crystal system in the space group P6 3 . Polycrystalline powders were synthesized to perform physical property measurements. Magnetic measurements over the 2–300 K temperature range reveal that Ce 3 Mg 0.5 SiSe 7 and Gd 3 Mg 0.5 SiSe 7 exhibit paramagnetic behavior with negative Weiss constants (θ W = −14.50, θ W = −6.13 K, respectively). The optical properties of RE 3 Mg 0.5 SiSe 7 (RE = Ce, Pr, Nd, Sm, Gd) were measured by ultraviolet–visible (UV–vis) diffuse reflectance. Density functional theory (DFT) electronic structure calculations were performed. Furthermore, a second harmonic generation measurement was performed on a polycrystalline powder of Ce 3 Mg 0.5 SiSe 7 and was found to be SHG active with an efficiency of 0.11 times the standard potassium dihydrogen phosphate (KDP).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Crystal Growth of Quaternary RE 2 EuSi 2 S 8 ( RE = Ce–Nd, Sm, Gd, Tb) Using Flux-Assisted Boron Chalcogen Mixture (BCM) Method: Investigation of Magnetic and Luminescence Properties

A series of quaternary rare-earth containing thiosilicates with the general formula RE 2 EuSi 2 S 8 (RE = Ce–Nd, Sm, Gd, Tb) has been synthesized via the flux-assisted boron chalcogen mixture (BCM) crystal growth method. High-quality single crystals were obtained, and their crystal structures were determined by single-crystal X-ray diffraction. The RE 2 EuSi 2 S 8 series crystallizes in the trigonal system, adopting the space group R-3c. Polycrystalline samples were employed for physical property measurements, including magnetic susceptibility measurements, UV–visible diffuse reflectance, and photoluminescent response. Magnetic data of RE 2 EuSi 2 S 8 ( RE = Ce, Nd, and Gd) were collected over the 2–300 K temperature range. The samples were paramagnetic behavior with negative Weiss constants (θ W = −11.05, −10.55, and −1.35 K respectively). Their thermal stability was investigated using thermogravimetric analysis (TGA). Optical band gaps, estimated from diffuse reflectance spectra, were determined to be 2.2(1) eV for Ce 2 EuSi 2 S 8 , 1.8(1) eV for Nd 2 EuSi 2 S 8 , and 1.7(1) eV for Gd 2 EuSi 2 S 8 respectively. Finally, photoluminescence measurements were collected on Ce 2 EuSi 2 S 8 and Tb 2 EuSi 2 S 8 single crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.

Biswas, Arpan [University of Tennessee, Knoxville,↗

DOE OSTI2025

Building workflows for an interactive human-in-the-loop automated experiment (hAE) in STEM-EELS

Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.

Pratiush, Utkarsh [Univ. of Tennessee, Knoxville, ↗

DOE OSTI2024

When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design

This review explores the intersection of bio-plausible artificial intelligence in the form of spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain, highlighting their collective potential for low-power edge computing environments. Through detailed investigation at the device, circuit, and system levels, we highlight the pivotal synergies between SNNs and IMC architectures. Additionally, we emphasize the critical need for comprehensive system-level analyses, considering the inter-dependencies among algorithms, devices, circuit, and system parameters, crucial for optimal performance. An in-depth analysis leads to the identification of key system-level bottlenecks arising from device limitations, which can be addressed using SNN-specific algorithm–hardware co-design techniques. This review underscores the imperative for holistic device to system design-space co-exploration, highlighting the critical aspects of hardware and algorithm research endeavors for low-power neuromorphic solutions.

Physics↗

DOE OSTI2024

Primeval very low-mass stars and brown dwarfs – VIII. The first age benchmark L subdwarf, a wide companion to a halo white dwarf

ABSTRACT We report the discovery of five white dwarf + ultracool dwarf systems identified as common proper motion wide binaries in the Gaia Catalogue of Nearby Stars. The discoveries include a white dwarf + L subdwarf binary, VVV 1256−62AB, a gravitationally bound system located 75.6$^{+1.9}_{-1.8}$ pc away with a projected separation of 1375$^{+35}_{-33}$ au. The primary is a cool DC white dwarf with a hydrogen dominated atmosphere, and has a total age of $10.5^{+3.3}_{-2.1}$ Gyr, based on white dwarf model fitting. The secondary is an L subdwarf with a metallicity of [M/H] = $-0.72^{+0.08}_{-0.10}$ (i.e. [Fe/H] = $-0.81\pm 0.10$) and $T_{\rm eff}$ = 2298$^{+45}_{-43}$ K based on atmospheric model fitting of its optical to near infrared spectrum, and likely has a mass just above the stellar/substellar boundary. The subsolar metallicity of the L subdwarf and the system’s total space velocity of 406 km s−1 indicates membership in the Galactic halo, and it has a flat eccentric Galactic orbit passing within 1 kpc of the centre of the Milky Way every $\sim$0.4 Gyr and extending to 15–31 kpc at apogal. VVV 1256−62B is the first L subdwarf to have a well-constrained age, making it an ideal benchmark of metal-poor ultracool dwarf atmospheres and evolution.

Zhang, Z. H. (ORCID:000000033047607X)↗