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DOE OSTI2026

Lightcurves, Rotation Periods, and Colors for Vera C. Rubin Observatory’s First Asteroid Discoveries

We present lightcurves, rotation periods, and colors for the first asteroid discoveries made with the NSF-DOE Vera C. Rubin Observatory. These are the first science results derived from the 2103 asteroid discoveries released as part of the Rubin First Look (RFL) media event on 2025 June 23, in which the first LSST Camera commissioning images were released. The ∼340,000 observations in which the discoveries were made span nine nights between 2025 April 21 and May 5. With a limiting single-epoch 5σ depth of ∼23–25 mag and dense temporal sampling under an irregular, commissioning-driven cadence, the RFL observations provide an ideal test bed for determination of rotation periods, including sensitivity to rapid rotation. We model lightcurves and derive rotation periods and colors for the ∼2000 objects. We find 75 main-belt asteroids (MBAs) and one near-Earth object (NEO) with reliable rotation periods spanning 0.031–21.3 hr and a photometric precision in the range of 0.05–0.15 mag. We find 19 superfast rotators with periods shorter than the 2.2 hr spin barrier. Rubin-discovered MBA 2025 MN 45 is the fastest-rotating d > 0.5 km known asteroid with a rotation period of 1.9 minutes; along with NEO 2025 MJ 71 (1.9 minutes) and Rubin-discovered MBAs 2025 MK 41 (3.8 minutes), 2025 MV 71 (13 minutes), and 2025 MG 56 (16 minutes), these five super- to ultrafast rotators join a couple of NEOs as the fastest-spinning subkilometer asteroids known. As this study demonstrates, even in early commissioning, Rubin is successfully probing a previously sparsely sampled region of the subkilometer size−spin rate regime for MBAs.

Greenstreet, Sarah [NSF National Optical-Infrared ↗

DOE OSTI2022

Lignin-First Biorefinery Development

Cost-effective biomass fractionation is an enabling, grand challenge for biorefining, especially when both carbohydrates and lignin are targeted for valorization. The advent of Reductive Catalytic Fractionation (RCF) - an active stabilization approach that solubilizes lignin from biomass and catalytically depolymerizes it into a narrow slate of monomers and oligomers - represents a potential step forward for this important goal. In the Lignin-First Biorefinery Development project, we are employing techno-economic analysis (TEA) and life cycle assessment (LCA) to guide bench-scale R&D efforts towards cost-effective RCF-based biorefining. This project is critical to lignin valorization efforts and could enable the use of woody feedstocks in a traditional biochemical conversion context. To date, we have conducted the first rigorous TEA and LCA study of the RCF process, developed a flow-through system to separate biomass and the chemical catalyst in RCF chemistry, developed models for solvolysis chemistry and transport phenomena for poplar, and co-led an international, authoritative perspective on guidelines for the research community on how to best practice lignin-first biorefining. The primary challenges for the lignin-first RCF process going forward are catalyst stability, the need to utilize, recover, and recycle high boiling point solvents to lower RCF reactor pressure, and the challenge of operating RCF continuously - all of which are being directly tackled by this project.

biomass↗

DOE OSTI2022

Methods and systems for electroless plating a first metal onto a second metal in a molten salt bath, and surface pretreatments therefore

Systems and methods for electroless plating a first metal onto a second metal in a molten salt bath including: a bath vessel holding a dry salt mixture including a dry salt medium and a dry salt medium of the first metal, and without the reductant therein, the dry salt mixture configured to be heated to form a molten salt bath; and the second metal is configured to be disposed in the molten salt bath and receive a pure coating of the first metal thereon by electroless plating in the molten salt bath, wherein the second metal is more electronegative than the first metal.

Freiderich, Melissa Ensor↗

DOE OSTI2023

Methods and systems for electroless plating a first metal onto a second metal in a molten salt bath, and surface pretreatments therefore

Freiderich, Melissa Ensor↗

DOE OSTI2025

First-Principles Cost Analysis of Advanced High-Temperature Nuclear Plants

Due to the vast number of recent nuclear reactor innovations, particularly those pertaining to generation IV reactor types like high-temperature gas cooled reactors (HTGRs) and sodium-cooled fast reactors (SFRs), and the newer deployment strategies envisioned, such as use of small modular reactors (SMRs) or even microreactors, reliable, detailed, and complete costs of these nuclear innovations are needed in wide availability. The types of models that generally achieve these objectives are those incorporating the fundamental nature of the real-world systems they aspire to predict, such as first-principles models. Furthermore, first principles models typically offer predictiveness that is not attained by most other types of individual-models. However, detailed, first-principles cost modeling of nuclear reactors and entire nuclear plants is relatively limited. To address this limitation in the availability of detailed, predictive models based on fundamentals, we recently developed a range of cost models, mostly based on first-principles methodologies, to project full lifecycle costs (LCCs) of nuclear power plants (NPPs) based on multiple parallel SM-HTG-pebble bed reactors (PBRs) and SM-SFRs.

Prosser, Jacob H. [Strategic Analysis, Inc., Arlin↗

DOE OSTI2022

First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions

The whole Al-Li phase diagram is predicted from first principles calculations and statistical mechanics including the effect of configurational and vibrational entropy. The formation enthalpy of different configurations at different temperatures was accurately predicted by means of cluster expansions that were fitted from first principles calculations. The vibrational entropic contribution of each configuration was determined from the bond length vs. bond stiffness relationships for each type of bond and the Gibbs free energy of the different phases was obtained as a function of temperature from Monte Carlo simulations. The predicted phase diagram was in excellent agreement with the currently accepted experimental one in terms of the stable (AlLi, Al 2 Li 3 , AlLi 2 , Al 4 Li 9 ) and metastable (Al 3 Li) phases, of the phase boundaries between them and of the maximum stability temperature of line compounds. In addition, it provided accurate information about the gap between Al 3 Li and AlLi solvus lines. Finally, the influence of the vibrational entropy on the correct prediction of the phase diagram is discussed. Overall, the methodology shows that accurate phase diagrams of alloys of technological interest can be predicted from first principles calculations.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Diffusion of alloying and fission atoms in α-U investigated by first-principles calculation

Stability and diffusion of small defect clusters containing alloying or fission atoms (“F” is used to represent both atoms) in an α-uranium (U) crystal are investigated using first-principles calculations. Here, results indicate that different stable defect structures are observed for an interstitial defect and a cluster (F n V m ) containing different alloying or fission atoms and vacancies. An interstitial defect is found to be most stable either at the center of a pyramid pentahedron or in a 〈010〉 mixed dumbbell configuration and the latter one is reported for the first time. Formation energy of a small F n V m cluster depends on the distribution of vacancies. Based on these stable defects, new migration energies and paths have been explored in this work. Anisotropic migration of an interstitial defect by jumping between nearest centers of two pyramid pentahedrons or jumping between two mixed U-Zr dumbbells has been suggested. Furthermore, a new migration-rotation mechanism has been explored for the first time for a FV 2 cluster, resulting in a 3D diffusion. Finally, the effect of substitutional Pu atoms on the migration of a FV 2 cluster suggests that optimizing the Pu concentration may prohibit the diffusion of some alloying or fission atoms, increasing the safe performance of metallic fuels.

3D diffusion↗

DOE OSTI2024

First-principles calculations of defects and electron–phonon interactions: Seminal contributions of Audrius Alkauskas to the understanding of recombination processes

First-principles calculations of defects and electron–phonon interactions play a critical role in the design and optimization of materials for electronic and optoelectronic devices. The late Audrius Alkauskas made seminal contributions to developing rigorous first-principles methodologies for the computation of defects and electron–phonon interactions, especially in the context of understanding the fundamental mechanisms of carrier recombination in semiconductors. Alkauskas was also a pioneer in the field of quantum defects, helping to build a first-principles understanding of the prototype nitrogen-vacancy center in diamond, as well as identifying novel defects. Here, we describe the important contributions made by Alkauskas and his collaborators and outline fruitful research directions that Alkauskas would have been keen to pursue. Audrius Alkauskas’ scientific achievements and insights highlighted in this article will inspire and guide future developments and advances in the field.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Gauge invariance of light-matter interactions in first-principle tight-binding models

In this work, we study the different ways of introducing light-matter interaction in first-principle tight-binding (TB) models. The standard way of describing optical properties is the velocity gauge, defined by linear coupling to the vector potential. In finite systems a transformation to represent the electromagnetic radiation by the electric field instead is possible, albeit subtleties arise in periodic systems. The resulting dipole gauge is a multi-orbital generalization of the Peierls substitution. In this work we investigate the accuracy for both pathways, with particular emphasis on gauge invariance, for TB models constructed from maximally localized Wannier functions. This approach accurately captures the light-matter interaction close to the Fermi level. Focusing on paradigmatic two-dimensional materials, we construct first-principle models and calculate the response to electromagnetic fields in linear response and for strong excitations. Benchmarks against fully converged first-principle calculations allow for ascertaining the accuracy of the TB models. We find that the dipole gauge provides a more accurate description than the velocity gauge in all cases. The main deficiency of the velocity gauge is an imperfect cancellation of paramagnetic and diamagnetic current. Formulating a corresponding sum rule however provides a way to explicitly enforce this cancellation. This procedure corrects the TB models in the velocity gauge, yielding excellent agreement with dipole gauge and thus restoring gauge invariance.

2-dimensional systems↗

DOE OSTI2020

Validating first-principles molecular dynamics calculations of oxide/water interfaces with x-ray reflectivity data

Metal oxide/water interfaces play a crucial role in many electrochemical and photocatalytic processes, such as photoelectrochemical water splitting, the creation of fuel from sunlight, and electrochemical CO 2 reduction. First-principles electronic structure calculations can reveal unique insights into these processes, such as the role of the alignment of the oxide electronic energy levels with those of liquid water. An essential prerequisite for the success of such calculations is the ability to predict accurate structural models of these interfaces, which in turn requires careful experimental validation. Here we report a general, quantitative validation protocol for first-principles molecular dynamics simulations of oxide/aqueous interfaces. The approach makes direct comparisons of interfacial x-ray reflectivity (XR) signals from experimental measurements and those obtained from ab initio simulations with semilocal and van der Waals functionals. The protocol is demonstrated here for the case of the Al 2 O 3 (001)/water interface, one of the simplest oxide/water interfaces. We discuss the technical requirements needed for validation, including the choice of the density functional, the simulation cell size, and the optimal choice of the thermodynamic ensemble. Our results establish a general paradigm for the validation of structural models and interactions at solid/water interfaces derived from first-principles simulations. Furthermore, while there is qualitative agreement between the simulated structures and the experimental best-fit structure, direct comparisons of simulated and measured XR intensities show quantitative discrepancies that derive from both bulk regions (i.e., alumina and water) as well as the interfacial region, highlighting the need for accurate density functionals to properly describe interfacial interactions. Our results show that XR data are sensitive not only to the atomic structure (i.e., the atom locations) but also to the electron-density distributions in both the substrate and at the interface.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Twin Boundaries Contribute to The First Cycle Irreversibility of LiNiO 2

Abstract LiNiO 2 remains a target for layered oxide Li‐ion cathode development as it can theoretically deliver the highest energy density of this materials class. In practice, LiNiO 2 suffers from poor capacity retention due to electrochemically‐induced structural changes. While the impact of Ni off‐stoichiometry on the electrochemical performance has been extensively studied, that of planar defects present in the as‐synthesized cathode is not well understood. Advanced ex situ and operando structure probes are used to identify and quantify point and planar defects present in as‐synthesized Li 1‐y Ni 1+y O 2 cathodes and monitor their evolution during the first cycle. Specifically, a 7 Li nuclear magnetic resonance (NMR) signature characteristic of Li environments near twin boundaries is identified; an assignment supported by first‐principles calculations and scanning transmission electron microscopy (STEM) images of twin boundary defects. The NMR results suggest that the concentration of twin boundaries depends on the amount of Ni excess. Moreover, operando magnetometry and ex situ synchrotron X‐ray diffraction and NMR demonstrate that these planar defects impede Li reinsertion into the bulk cathode at reasonable discharge rates and contribute to the first cycle irreversible capacity. These findings provide new design rules for Li 1‐y Ni 1+y O 2 cathodes, whereby a reduced concentration of twin boundaries in the pristine material leads to reduced kinetic limitations and improved cathode utilization.

Nguyen, H.↗

DOE OSTI2020

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (similar to 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. In this work, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (similar to 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with similar to 500 total reference calculations. Using an extensive DFT test set of similar to 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/angstrom of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Enabling power measurement and control on Astra: The first petascale Arm supercomputer

Astra, deployed in 2018, was the first petascale supercomputer to utilize processors based on the ARM instruction set. The system was also the first under Sandia's Vanguard program which seeks to provide an evaluation vehicle for novel technologies that with refinement could be utilized in demanding, large-scale HPC environments. In addition to ARM, several other important first-of-a-kind developments were used in the machine, including new approaches to cooling the datacenter and machine. Here we document our experiences building a power measurement and control infrastructure for Astra. While this is often beyond the control of users today, the accurate measurement, cataloging, and evaluation of power, as our experiences show, is critical to the successful deployment of a large-scale platform. While such systems exist in part for other architectures, Astra required new development to support the novel Marvell ThunderX2 processor used in compute nodes. In addition to documenting the measurement of power during system bring up and for subsequent on-going routine use, we present results associated with controlling the power usage of the processor, an area which is becoming of progressively greater interest as data centers and supercomputing sites look to improve compute/energy efficiency and find additional sources for full system optimization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

First saturation correction in high energy proton-nucleus collisions. Part I. Time evolution of classical Yang-Mills fields beyond leading order

In high energy proton-nucleus collisions, the single- and double-inclusive soft gluon productions at the leading order have been calculated and phenomenologically studied in various approaches for many years. These studies do not take into account the saturation and multiple rescatterings in the field of the proton. The first saturation correction to these leading order results (the terms that are enhanced by the combination \( {\alpha}_s^2{\mu}^2 \) , where μ 2 is the proton’s color charge squared per unit transverse area) has not been completely derived despite recent attempts using a diagrammatic approach. This paper is the first in a series of papers towards analytically completing the first saturation correction to physical observables in high energy proton-nucleus collisions. Our approach is to analytically solve the classical Yang-Mills equations in the dilute-dense regime using the Color Glass Condensate effective theory and compute physical observables constructed from classical gluon fields. In the current paper, the Yang-Mills equations are solved perturbatively in the field of the dilute object (the proton). Next-to-leading order and next-to-next-to-leading order analytic solutions are explicitly constructed. A systematic way to obtain all higher order analytic solutions is outlined.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Revisiting the first Sandia Fracture Challenge with transient deformation heating and strain localization considerations

In this study, we present a systematic experimental study and accompanying theoretical analysis of the dependence of ductile fracture on strain localization, strain hardening rates, deformation-induced thermal softening, and transient heat conduction under spatially uniform as well as spatially heterogeneous deformation. Spatially uniform cases are studied via standard dogbone shaped specimens subject to uniaxial tension. Spatially heterogeneous deformation is studied via the so-called Sandia Fracture Challenge (SFC) specimen, which is a standard compact tension specimen modified with three machined holes in front of a blunt notch. We utilize the same precipitation hardened martensitic stainless steel used in the first SFC experiments, i.e. 15-5 PH with an H1075 heat treatment. We also study 15-5 PH in Condition A and with the H900 heat treatment, each of which has a different hardening behavior and ductility. We find that the ductility does not correlate with the deformation to first crack initiation in the SFC specimen. Instead, hardening rates are better correlated. Moreover, a re-examination finds that an accurate calibration of the hardening rates is strongly correlated with accurate blind predictions of ductile fracture in the SFC specimens. Given that thermal softening can greatly affect hardening rates, we provided a thermographic analysis of deformation-induced heating and transient heat transfer in both the dogbone shaped samples and the SFC specimens. Transient heating in stainless steels is found to have first-order effects on the strain at the onset of necking, strain to fracture, as well as strain localization and ductile fracture in complex geometries even under so-called quasi-static loading rates.

42 ENGINEERING↗

DOE OSTI2023

Extrapair offspring of the blue-footed booby show no sign of higher fitness in the first 10 years of life

According to the good genes and genetic compatibility hypotheses, females of socially monogamous species obtain genetic benefits for their offspring by performing extrapair copulations with males of higher quality than their social mates or males with whom they are more genetically compatible. If extrapair offspring do receive genetic benefits in the form of advantageous alleles or more compatible allele combinations, they should outperform their within-pair half-siblings' survival and/or reproductive output. Here, in this study, we followed 52 extrapair and 737 within-pair blue-footed booby, Sula nebouxii, offspring during their first 10 years of life (excluding the embryonic period and the first 10 days after hatching) to assess whether they differed in fledging probability, fledgling body condition, recruitment probability, age at first reproduction, number of breeding events or accumulated breeding success. Extrapair and within-pair offspring did not differ in any of these proxies of fitness. Furthermore, we found that extrapair offspring were equally likely to occur in any hatching position. However, differences in fledgling production over the lifetime could not be ruled out, and because only within-pair production of eggs and fledglings was tallied, the possibility remains that extrapair offspring could produce more extrapair offspring later in life than do within-pair offspring. Furthermore, the possibility of context-dependent genetic benefits occurring only under stressful conditions cannot be discounted because our sample of offspring was obtained in a single exceptionally favourable reproductive season.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

On restricting first order form of gauge theories to one-loop order

Highlights: • We prove the equivalence of the first and second-order formalism. • We use Lagrange multiplier fields to limit radiative effects to one-loop order. • We prove that the quantized theory is both renormalizable and unitary. The first order form of the Yang–Mills and Einstein–Hilbert actions are quantized, and it is shown how Green’s functions computed using the first and the second order form of these theories are related. Next we show how by use of Lagrange multiplier fields (LM), radiative effects beyond one-loop order can be eliminated. This allows one to compute Green’s functions exactly without loss of unitarity. The consequences of this restriction on radiative effects are examined for the Yang–Mills and Einstein–Hilbert actions. In these two gauge theories, we find that the quantized theory is both renormalizable and unitary once the LM field is used to eliminate effects beyond one-loop order.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

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↗