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DOE OSTI2026

Surface Terminations of LaAlO 3 Perovskite Nanoparticles as Viewed by Solid-State Nuclear Magnetic Resonance

Nanocrystal surfaces generally undergo reconstructions that differentiate them from the bulk structures, often in nontrivial ways. Understanding these terminations is critical across diverse fields, from heterogeneous catalysis to the formation of topological states and the synthesis of semiconductor nanomaterials. Determining surface structures is currently an interdisciplinary task, most often involving high-resolution electron microscopy and surface electron diffraction. These methods, however, do not provide a global view of the ensemble of structures present in a sample. Here, we show how surface-sensitive solid-state nuclear magnetic resonance (SSNMR) spectroscopy methods can bridge this gap. In this context, we investigated the surface structure of lanthanum aluminate (LaAlO 3 ) perovskite nanoparticles. Four distinct surface terminations have previously been observed for this material, but their relative abundances were unknown. Using an array of double- and triple-resonance SSNMR methods probing the relative proximities of surface 1 H, 27 Al, 17 O, and 139 La nuclei, we conclude the surface to be majority terminated (80%) by AlO x with substantial (20%) LaO x terminated regions.

Materials↗

DOE OSTI2025

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗

DOE OSTI2024

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

25 ENERGY STORAGE↗

DOE OSTI2020

How magnetic field can transform a superconductor into a Bose metal

Abstract We discuss whether a simple theory of superconducting stripes coupled by Josephson tunneling can describe a metallic transport, once the coherent tunneling of pairs is suppressed by the magnetic field. For a clean system, the conclusion we reached is negative: the excitation spectrum of preformed pairs consists of Landau levels, and once the magnetic field exceeds a critical value, the transport becomes insulating. As a speculation, we suggest that a Bose metal can exist in disordered systems provided that the disorder is strong enough to localize some pairs. Then the coupling between propagating and localized pairs broadens the Landau levels, resulting in a metallic conductivity. Our model respects the particle–hole symmetry, which leads to a zero Hall response. And intriguingly, the resulting anomalous metallic state has no Drude peak and the spectral weight of the cyclotron resonance vanishes at low temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Polarization formalism for ALP-induced X-ray emission from magnetars

Missions like NASA's Imaging X-ray Polarimetry Explorer (IXPE) are poised to provide an unprecedented view of the Universe in polarized X-rays. Polarization probes physical anisotropies, a fact exploited by particle physicists to look for the anisotropic a E∙B operator in the axion-like-particle (ALP) Lagrangian. Such studies have typically focused on polarization in the radio and microwaves, through local or cosmic birefringence effects. To such polarization studies we add X-rays emanating from magnetars — a class of neutron stars with near-critical strength magnetic fields — that are important targets for IXPE. ALPs produced in the neutron star core convert to X-rays in the magnetosphere; such X-rays are polarized along the direction parallel to the dipolar magnetic field at the point of conversion. Here, we develop the full theoretical formalism for ALP-induced polarization in the presence of dipolar magnetic fields. For uncorrelated photon and ALP production mechanisms, we completely disentangle the ALP contributions to the Stokes parameters in terms of the ALP intensity, the ALP-to-photon conversion probability, and the ALP-induced birefringence. In the proper limit, our results demonstrate that the inclusion of ALPs suppresses the observed degree of circular polarization compared to its pure astrophysical value. Our results can also be used to impose limits on ALP couplings with IXPE polarization data from magnetars 4U 0142+61 and 1RXS J170849.0-400910, the subject of upcoming work.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Variational quantum simulation of the critical Ising model with symmetry averaging

Here we investigate the use of deep multiscale entanglement renormalization ansatz (DMERA) circuits as a variational ansatz. We use the exactly solvable one-dimensional critical transverse-field Ising model as a test bed. Numerically exact simulation of the quantum circuit ansatz can in this case be carried out to hundreds of qubits by exploiting efficient classical algorithms for simulating matchgate circuits. We find that, for this system, the DMERA strongly outperforms a standard quantum approximate optimization algorithm (QAOA)–style ansatz, and that a major source of systematic error in correlation functions approximated using the DMERA is the breaking of the translational and Kramers-Wannier symmetries of the transverse-field Ising model. We are able to reduce this error by up to four orders of magnitude by symmetry averaging, without incurring additional cost in qubits or circuit depth. Here, we propose that this technique for mitigating systematic error could be applied to noisy intermediate-scale quantum (NISQ) simulations of physical systems with other symmetries.

1-dimensional spin chains↗

DOE OSTI2021

Nonlinear Hall effect in Weyl semimetals induced by chiral anomaly

In this work, we predict a nonlinear Hall effect in certain Weyl semimetals with broken inversion symmetry. When the energy dispersions about pairs of Weyl nodes are skewed-the Weyl cones are "tilted"-the concerted actions of the anomalous velocity and the chiral anomaly give rise to the nonlinear Hall effect. This Hall conductivity is linear in both electric and magnetic fields and depends critically on the tilting of the Weyl cones. We also show that this effect does not rely on a finite Berry curvature dipole, in contrast with the intrinsic quantum nonlinear Hall effect that was recently observed in type-II Weyl semimetals.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Pinning Characteristics of Zr and Hf- Added REBCO Coated Conductors Made by Advanced MOCVD in Low-to-High Magnetic Fields

BaMO 3 (M=Zr, Hf) pinning centers introduced in REBa 2 Cu 3 O 7-x (REBCO and RE = rare earth) coated conductors yield superior performance in high magnetic fields. We present the critical current density J c over a temperature range of 4.2-77 K and magnetic fields of 0-14 T (B || c-axis), of 5-15 mol.% Zr- and Hf-added REBCO 4+ μm thick film tapes fabricated by advanced metal organic chemical vapor deposition (A-MOCVD). The morphology of self-assembled BMO nanorods aligned along c-axis is found to be dependent on the Zr/Hf content. We also observe a correlation between the density of RE 2 O 3 in-plane nano-precipitates and the continuity and concentration of BMO nanorods in films of different Ba content. It is found that the (Ba+M)/Cu (M=Zr, Hf) content in REBa 2 Cu 3 O 7-x affects the shape of pinning force density curves over a wide magnetic field range and temperatures below 20 K. Another remarkable observation is the similarity in the critical current properties of Hf and Zr -added REBCO films as function of (Ba+M)/Cu content at intermediate to high range of (Ba+M)/Cu content. As a result, a quantitative analysis of pinning efficiency and correlation with the microstructure of Zr and Hf- added REBCO coated conductors is discussed.

2G high-temperature superconductor (2G-HTS)↗

DOE OSTI2020

29Si solid state NMR and Ti K-edge XAFS pre-edge spectroscopy reveal complex behavior of Ti in silicate melts

Abstract An understanding of the mechanisms of Ti is incorporation into silicate glasses and melts is critical for the field of petrology. Trace-element thermobarometry, high-field-strength element partitioning, and the physical properties of magmas are all be influenced by Ti incorporation into glasses and changes therein in response to changes in composition and temperature. In this study, we combine 29 Si solid state NMR and Ti K -edge XAFS spectroscopy to investigate how Ti is incorporated into quenched Na-silicate glasses, and the influence of Ti on the structure of silicate species in these glasses. 29 Si NMR shows that in both Ti-bearing Na 2 O•4SiO 2 (NS4) and Na 2 O•8SiO 2 (NS8) glasses, increasing the amount of Ti in the melt results in a shift of Si Q 4 peak in the 29 Si NMR spectra reflecting Ti nearest neighbors for Si in Q 4 speciation. The Ti XAFS results from NS8 glass indicate that Ti is primarily incorporated in [5]-fold coordination. At higher Ti content, there is a shift of the XAFS pre-edge feature suggesting mixing of [4]-fold Ti into the spectra. Combined, the 29 Si NMR and XAFS pre-edge data are consistent with Ti incorporation as isolated [5] Ti atoms and the formation of [5] Ti clusters at relatively low Ti concentrations, with no evidence for Ti–Na interactions as suggested by previous studies. As the Ti content increases, the Ti atoms begin to occupy 4-fold coordinated sites interacting primarily with Si in Q 4 speciation (no significant Na– [4] Ti bonding). The internal consistency of these two techniques provides a uniquely complete snapshot of the complexity of Ti incorporation in silicate melts and underlies the importance of understanding Ti incorporation mechanisms in natural magmatic systems.

Ackerson, Michael R.↗

DOE OSTI2021

Surface Sampling Techniques for the Canister Deposition Field Demonstration

This report describes plans for dust sampling and analysis for the multi-year Canister Deposition Field Demonstration. The demonstration will use three commercial 32PTH2 NUHOMS welded stainless steel storage canisters, which will be stored at an ISFSI site in Advanced Horizontal Storage Modules. One canister will be unheated; the other two will have heaters to achieve canister surface temperatures that match, to the degree possible, spent nuclear fuel (SNF) loaded canisters with heat loads of 10 kW and 40 kW. Surface sampling campaigns will take place on a yearly or bi-yearly basis. The goal of the planned dust sampling and analysis is to determine important environmental parameters that impact the potential occurrence of stress corrosion cracking on SNF dry storage canisters. Specifically, the size, morphology, and composition of the deposited dust and salt particles will be quantified, as well as the soluble salt load per unit area and the rate of deposition, as a function of canister surface temperature, location, time, and orientation. Sampling locations on the canister surface will nominally include 25 locations, corresponding to 5 circumferential locations at each of the 5 longitudinal locations. At each sampling location, a 2x2 sampling grid (containing 4 sample cells) will be painted onto the metal surface. During each sampling campaign, two samples at each sampling location will be collected, in a specific routine to measure both periodic (yearly or bi-yearly) and cumulative deposition rates. For each sample, a wet and a dry sample will be collected. Wet samples will be analyzed to determine the composition of the soluble salt fraction and to estimate salt loading per unit area. Dry samples will be analyzed to assess particle size, morphology, mineralogy, and identity (e.g. for floral/faunal fragments). The data generated by this proposed sampling plan will provide detailed information on dust and salt aerosol deposits on spent nuclear fuel canister surfaces. The anticipated results include information regarding particle compositions, size distributions, and morphologies, in addition to particle deposition rates as a function of canister surface location, orientation, time, and temperature. The information gathered during the Canister Deposition Field Demonstration is critical for ongoing efforts to develop a detailed understanding of the potential for stress corrosion cracking on SNF dry storage canisters

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Saturated Bioenergy Buffers: Site Suitability Classification and Estimated Areas of Candidate Sites in the U.S. Midwest Under Three Scenarios

The loss of nutrients applied to tile-drained row-crop fields is a critically important component of agriculture’s impact on surface water quality, because drain tiles provide a short circuit to ditches and creeks, resulting in rapid nutrient loss. This nutrient loss ultimately advances eutrophication and hypoxia, i.e., the creation of dead zones in bodies of water, both locally and regionally (for example, in western Lake Erie and the Gulf of Mexico). Saturated buffers help address the tile-drainage water quality problem, and incorporating bioenergy crops into saturated bioenergy buffers could provide both environmental protection and an additional source of income for farmers.

09 BIOMASS FUELS↗

DOE OSTI2026

Lessons Learned from Ecosystem-Scale Experimental Field Studies (Workshop Report)

Efforts to understand and predict ecosystem responses to environmental change require long-term, large-scale, spatially representative experiments and observations that capture natural variability, test predictive models, and generate transferable knowledge. Such studies are indispensable for unraveling the complexities of terrestrial ecosystems and their responses to disturbances and evolving environmental conditions, while generating the data necessary for developing mechanistic models and predictive tools that inform decision-making processes. Having a rich history of designing and executing large-scale ecosystem experiments, the U.S. Department of Energy’s Environmental System Science program convened a workshop in January 2025 that brought together leaders in the field to distill critical lessons from decades of experience in large-scale experiments. The workshop aimed to (1) provide an ecosystem experiment primer for best practices, thus ensuring a high scientific return on investment for funding agencies, and (2) offer a robust framework for the design and management of future research initiatives. This report synthesizes insights and experiences from workshop participants and is structured to capture the entire research life cycle, from goal setting and design to operations, adaptive management, team dynamics, collaborations, and the often overlooked aspect of decommissioning. By synthesizing decision-making and lessons learned across diverse research approaches, the report aims to provide a template of essential factors to consider when designing successful long-term, large-scale ecosystem experiments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Stoichiometric Thiol Redox Proteomics for Quantifying Cellular Responses to Perturbations

Post-translational modifications regulate the structure and function of proteins that can result in changes to the activity of different pathways. These include modifications altering the redox state of thiol groups on protein cysteine residues, which are sensitive to oxidative environments. While mass spectrometry has advanced the identification of protein thiol modifications and expanded our knowledge of redox-sensitive pathways, the quantitative aspect of this technique is critical for the field of redox proteomics. In this review, we describe how mass spectrometry-based redox proteomics has enabled researchers to accurately quantify the stoichiometry of reversible oxidative modifications on specific cysteine residues of proteins. We will describe advancements in the methodology that allow for the absolute quantitation of thiol modifications, as well as recent reports that have implemented this approach. We will also highlight the significance and application of such measurements and why they are informative for the field of redox biology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗

DOE OSTI2023

Green Computing Opportunities & Strategy

Computation is critical to emerging fields of data intensive research, enabling new methodologies, approaches and tools. As the rate of hardware efficiency gains slows, computational time and energy costs increase. To match pace with computation demand, new approaches are needed to keep the opportunity for impact open. Charles Tripp, lead of the Green Computing Catalyzer, discusses research efforts to improve software efficiency to enable faster, less energy-intensive computing.

algorithmic efficiency↗

DOE OSTI2025

Searches for New Physics With Muon Conversion at Fermilab and Triboson Production at the LHC

We report on several efforts to search for physics beyond the standard model of particle physics at broad energy scales. The Mu2e experiment at Fermilab will search for charged lepton flavor violation via the muon to electron conversion process, which is suppressed in the Standard Model. Mu2e will be operated at a low energy, yet can probe New Physics at very high mass scales (O(1e3 - 1e4 ) TeV). At high energies, the CMS experiment at the CERN LHC continues to deliver an impressive suite of Standard Model measurements and limits on a variety of New Physics signatures. Mu2e is under construction and slated to collect its first physics data in the coming years. This thesis describes work done during the construction phase of Mu2e and focuses on two critical areas: magnetic field modeling and statistical analysis. We describe a novel method for field modeling which we validate using a simulated dataset representing the expected magnetic field in the Detector Solenoid. This method blends a standard least-squares fitting technique that utilizes physically motivated analytical model functions with a novel physics informed network that is constructed to obey Maxwell’s equations. We show the technique can model the field with an accuracy of 10−7 despite the presence of injected noise in the pseudo-measurements at the 10−5 level. We then present preliminary results of the calibration of 3D Hall probes at the sub-10−4 level. These probes will be used to directly measure the Mu2e Detector Solenoid magnetic field on a sparse grid; these measurements serve as the input to the field model fitting. Finally, we describe the first implementation of both an unbinned shape analysis and a Bayesian interpretation applied to Mu2e pseudo-data. Up to 20% tighter limits can be set by the shape analysis compared to a standard cut & count analysis. The AlCap experiment collected data at PSI in 2015 to measure several important quantities related to nuclear muon capture on an aluminum target, which is a significant background process for Mu2e. The neutron emission from muon capture can introduce background hits in the Mu2e detectors and can increase radiation damage in various elements of the apparatus. We present measurements of the neutron group fluence and mean neutron multiplicity for muon capture on aluminum nuclei. Finally, we discuss an analysis of triboson production at CMS using an Effective Field Theory framework. Standard Model triboson production, which was first observed at CMS in 2020, has a relatively small cross section and provides direct access to both anomalous triple gauge couplings and quartic gauge couplings. These couplings, interpreted in the Standard Model Effective Field Theory, are studied in the present work. We target the boosted regime where the background rate is low and yields are enhanced when dimension-6 and dimension-8 Wilson coefficients are non-zero. We do not observe an excess in the data and therefore set bounds on the Wilson coefficients. For dimension-6 coefficients the tightest observed (expected) bounds are set on cW /Λ2 where Λ is the mass scale of new physics; the bounds are [−0.13, 0.12] TeV−2 ([−0.12, 0.12] TeV−2 ) at 95% CL. The tightest bounds in dimension-8 are set on fT,0 / Λ4 ; the observed (expected) bounds at 95% CL are [−0.63, 0.69] TeV−4 ([−0.54, 0.62] TeV−4 ). Additional results are presented which include scenarios where multiple Wilson coefficients are non-zero, the application of signal model clipping to address unitarity violation in Effective Field Theories, and a novel template fit developed for easier reinterpretation of our results.

Kampa, Cole Erik [Northwestern U. (main)] (ORCID:0↗

DOE OSTI2022

Superconductivity above 200 K discovered in superhydrides of calcium

Searching for superconductivity with T c near room temperature is of great interest both for fundamental science & many potential applications. Here we report the experimental discovery of superconductivity with maximum critical temperature (T c ) above 210 K in calcium superhydrides, the new alkali earth hydrides experimentally showing superconductivity above 200 K in addition to sulfur hydride & rare-earth hydride system. The materials are synthesized at the synergetic conditions of 160~190 GPa and ~2000 K using diamond anvil cell combined with in-situ laser heating technique. The superconductivity was studied through in-situ high pressure electric conductance measurements in an applied magnetic field for the sample quenched from high temperature while maintained at high pressures. The upper critical field Hc(0) was estimated to be ~268 T while the GL coherent length is ~11 Å. The in-situ synchrotron X-ray diffraction measurements suggest that the synthesized calcium hydrides are primarily composed of CaH 6 while there may also exist other calcium hydrides with different hydrogen contents.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗