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DOE OSTI2023

Dislocation generation in diamond under extreme loading

Diamond is, by virtue of its high bonding forces and Peierls-Nabarro barrier stresses, among the hardest materials on earth due to the difficulty of generating and moving dislocations. We demonstrate, using molecular dynamics and analytical calculations, that the generation of defects is dependent on loading orientation. Shock-loading single-crystal diamond along [001] and [011] to a stress of 137 GPa did not reveal dislocations, whereas loading along [111] generated profuse dislocations. The introduction of a void at the latter orientation generated dislocations at 72.6 GPa. Two slip systems were identified: <011>{100} and <112>{111}. The threshold for plastic deformation in diamond is orientation dependent and significantly lower than reported in previous studies: 0.14 G (shear modulus). The void collapse generated localized amorphization for [001]. In conclusion, these results on the anisotropy of plasticity and void effects have relevance for the improvement of symmetry in the collapse of diamond capsules in inertial fusion experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Design, construction, and characterization of a compact DD neutron generator designed for 40Ar/39Ar geochronology

A next-generation, high-flux DD neutron generator has been designed, commissioned, and characterized, and is now operational in a new facility at the University of California Berkeley. The generator, originally designed for 40 Ar/ 39 Ar dating of geological materials, has since served numerous additional applications, including medical isotope production studies, with others planned for the near future. In this work, we present an overview of the High Flux Neutron Generator (HFNG) which includes a variety of simulations, analytical models, and experimental validation of results. Extensive analysis was performed in order to characterize the neutron yield, flux, and energy distribution at specific locations where samples may be loaded for irradiation. A notable design feature of the HFNG is the possibility for sample irradiation internal to the cathode, just 8 mm away from the neutron production site, thus maximizing the neutron flux (n/cm 2 /s). The generator’s maximum neutron flux at this irradiation position is 2.58 x 10 7 n/cm 2 /s ± 5% (approximately 3 x 10 8 n/s total yield) as measured via activation of small natural indium foils. However, future development is aimed at achieving an order of magnitude increase in flux. Additionally, the deuterium ion beam optics were optimized by simulations for various extraction configurations in order to achieve a uniform neutron flux distribution and an acceptable heat load. Finally, experiments were performed in order to benchmark the modeling and characterization of the HFNG.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

13 HYDRO ENERGY↗

DOE OSTI2017

Solid waste generation and characterization in the University of Lagos for a sustainable waste management

Waste characterization is the first step to any successful waste management policy. In this paper, the characterization and the trend of solid waste generated in University of Lagos, Nigeria was carried out using ASTM (D5231-92) and Resource Conservation Reservation Authority RCRA Waste Sampling Draft Technical Guidance methods. The recyclable potential of the waste is very high constituting about 75% of the total waste generated. The estimated average daily solid waste generation in Unilag Akoka campus was estimated to be 32.2 tons. The solid waste characterization was found to be: polythene bags 24% (7.73 tons/day), paper 15% (4.83 tons/day), organic matters 15%, (4.83 tons/day), plastic 9% (2.90 tons/day), inert materials 8% (2.58 tons/day), sanitary 7% (2.25 tons/day), textile 7% (2.25 tons/day), others 6% (1.93 tons/day), leather 4% (1.29 tons/day) metals 3% (0.97 tons/day), glass 2% (0.64 tons/day) and e-waste 0% (0.0 tons/day). The volume and distribution of polythene bags generated on campus had a positive significant statistical correlation with the distribution of commercial and academic structures on campus. Waste management options to optimize reuse, recycling and reduce waste generation were discussed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Generation of radioisotopes for medical applications using high-repetition, high-intensity lasers

We used the PW high-repetition laser facility VEGA-3 at Centro de Láseres Pulsados in Salamanca, with the goal of studying the generation of radioisotopes using laser-driven proton beams. Various types of targets have been irradiated, including in particular several targets containing boron to generate α-particles through the hydrogen–boron fusion reaction. We have successfully identified γ-ray lines from several radioisotopes created by irradiation using laser-generated α-particles or protons including 43 Sc, 44 Sc, 48 Sc, 7 Be, 11 C and 18 F. We show that radioisotope generation can be used as a diagnostic tool to evaluate α-particle generation in laser-driven proton–boron fusion experiments. We also show the production of 11 C radioisotopes, ≈ 6 × 10 6 , and of 44 Sc radioisotopes, ≈ 5 × 10 4 per laser shot. This result can open the way to develop laser-driven radiation sources of radioisotopes for medical applications.

gamma ray spectroscopy↗

DOE OSTI2019

Computational Study of Short-Pulse Laser-Induced Generation of Crystal Defects in Ni-Based Single-Phase Binary Solid–Solution Alloys

Single-phase concentrated solid–solution alloys exhibit enhanced mechanical characteristics and radiation damage resistance, making them promising candidate materials for applications involving an exposure to rapid localized energy deposition. We use large-scale atomistic modeling to investigate the mechanisms of the generation of vacancies, dislocations, stacking faults, and twin boundaries in Ni, Ni 50 Fe 50 , Ni 80 Fe 20 , and Ni 80 Cr 20 targets irradiated by short laser pulses in the regime of melting and resolidification. The decrease in the thermal conductivity and strengthening of the electron–phonon coupling due to the intrinsic chemical disorder in the solid-solution alloys are found to have important implications on localization of the energy deposition and generation of thermoelastic stresses. The interaction of the laser-induced stress waves with the melting front is found to play a key role in roughening of the crystal–liquid interface and generation of dislocations upon the solidification. A common feature revealed in the structural analysis of all irradiated targets is the presence of high vacancy concentrations exceeding the equilibrium values at the melting temperature by about an order of magnitude. On the basis of the results of molecular dynamics simulations of solidification occurring at fixed levels of undercooling, the generation of vacancies is correlated with the velocity of the solidification front, and the processes responsible for creating the strong vacancy supersaturation are revealed. The suppression of the vacancy generation in the solid–solution alloys is also revealed and related to combined effect of enhanced vacancy mobility and higher energy of the vacancy formation in the alloy systems. The analysis of the first atomic shells surrounding the vacancy sites in Ni–Fe alloys uncovers the preference for the vacancy sites to be surrounded by Fe atoms and suggests that atomic-scale chemical heterogeneities may play an important role in defining the behavior and properties of the single-phase concentrated solid-solution alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Strong Surface-Enhanced Coherent Phonon Generation in van der Waals Materials

Terahertz (THz) coherent phonons have emerged as promising candidates for the next generation of high-speed, low-energy information carriers in atomically thin phononic or phonon-integrated on-chip devices. However, effectively manipulating THz coherent phonons remains a significant challenge. Here, in this study, we investigated THz coherent phonon generation in exfoliated van der Waals (vdW) flakes of Fe 3 GeTe 2 , Fe 5 GeTe 2 , and FePS 3 . We successfully generated the THz A 1g coherent phonon mode in these vdW flakes. An innovative approach involved partially exfoliating vdW flakes on a gold substrate and partially on a silicon (Si) substrate to compare the THz coherent phonon generation between both sides. Interestingly, we observed a significantly enhanced THz coherent phonon in the vdW/gold area compared with that in the vdW/Si area. Frequency-domain Raman mapping across the vdW flakes corroborated these findings. Numerical simulations further indicated a stronger enhanced surface field in vdW/gold structures than in vdW/Si structures. Consequently, we attribute the observed enhancement in THz coherent phonon generation to the increased surface field on the gold substrate. This enhancement was consistent across the three different vdW materials studied, suggesting the universality of this strategy. Our results hold promise for advancing the design of THz phononic and phonon-integrated devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Subpicosecond Optical Stress Generation in Multiferroic BiFeO 3

We report optical excitation leads to ultrafast stress generation in the prototypical multiferroic BiFeO 3 . The time scales of stress generation are set by the dynamics of the population of excited electronic states and the coupling of the electronic configuration to the structure. X-ray free-electron laser diffraction reveals high-wavevector subpicosecond-time scale stress generation following ultraviolet excitation of a BiFeO 3 thin film. Stress generation includes a fast component with a 1/e rise time with an upper limit of 300 fs and longer-rise time components extending to 1.5 ps. The contributions of the fast and delayed components vary as a function of optical fluence, with a reduced a fast-component contribution at high fluence. The results provide insight into stress-generation mechanisms linked to the population of excited electrons and point to new directions in the application of nanoscale multiferroics and related ferroic complex oxides. The fast component of the stress indicates that structural parameters and properties of ferroelectric thin film materials can be optically modulated with 3 dB bandwidths of at least 0.5 THz.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

FunDiff: diffusion models over function spaces for physics-informed generative modeling

Recent advances in generative modeling-particularly diffusion models and flow matching-have been widely used for synthesizing discrete data such as images and videos. However, adapting these models to physical applications remains challenging, as the quantities of interest are continuous functions governed by complex physical laws. To address this, we introduce FunDiff, an efficient and robust framework for generative modeling in function spaces. FunDiff combines a latent diffusion process with a function autoencoder architecture to handle input functions with varying discretizations, generates continuous functions that can be evaluated at arbitrary locations, and seamlessly incorporate physical priors. These priors are enforced through architectural constraints or physics-informed loss functions, ensuring that generated samples satisfy fundamental physical laws. We theoretically establish minimax optimality guarantees for density estimation in function spaces, demonstrating that diffusion-based estimators achieve optimal convergence rates under suitable regularity conditions. We further demonstrate the practical effectiveness of FunDiff across diverse applications in fluid dynamics and solid mechanics. Empirical results indicate that our method can generate physically consistent samples with high fidelity to the target distribution, and exhibit robustness to noisy and low-resolution data.

Wang, Sifan [Yale University, New Haven, CT (Unite↗

DOE OSTI2021

Artificial generation of representative single Li-ion electrode particle architectures from microscopy data

Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling. Information is drawn from multimodal microscopy techniques to artificially generate LiNi 0.5 Mn 0.3 Co 0.2 O 2 particles with full sub-particle grain detail. Statistical representations of particle architectures are derived from X-ray nano-computed tomography data supporting an ‘outer shell’ model, and sub-particle grain representations are derived from focused-ion beam electron backscatter diffraction data supporting a ‘grain’ model. A random field model used to characterize and generate the outer shells, and a random tessellation model used to characterize and generate grain architectures, are combined to form a multi-scale model for the generation of virtual electrode particles with full-grain detail. This work demonstrates the possibility of generating representative single electrode particle architectures for modeling and characterization that can guide synthesis approaches of particle architectures with enhanced performance.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Structural constraint integration in a generative model for the discovery of quantum materials

Billions of organic molecules have been computationally generated, yet functional inorganic materials remain scarce due to limited data and structural complexity. Here, in this work, we introduce Structural Constraint Integration in a GENerative model (SCIGEN), a framework that enforces geometric constraints, such as honeycomb and kagome lattices, within diffusion-based generative models to discover stable quantum materials candidates. SCIGEN enables conditional sampling from the original distribution, preserving output validity while guiding structural motifs. This approach generates ten million inorganic compounds with Archimedean and Lieb lattices, over 10% of which pass multistage stability screening. High-throughput density functional theory calculations on 26,000 candidates shows over 95% convergence and 53% structural stability. A graph neural network classifier detects magnetic ordering in 41% of relaxed structures. Furthermore, we synthesize and characterize two predicted materials, TiPd 0.22 Bi 0.88 and Ti 0.5 Pd 1.5 Sb, which display paramagnetic and diamagnetic behaviour, respectively. Our results indicate that SCIGEN provides a scalable path for generating quantum materials guided by lattice geometry.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Forty-year hydropower generation reanalysis for Conterminous United States

First published in 2022, the RectifHyd dataset provides hydrologically consistent estimates of monthly net generation for approximately 1,500 hydropower plants in the United States, addressing a gap in industrial surveys that have collected monthly generation data from only ~10% of plants post-2003. Here we present RectifHydPlus—an extended and enhanced dataset that improves on both the proxy information and temporal downscaling methodology adopted in RectifHyd. In addition to providing updated estimates of historical monthly generation for 590 plants with >10 MW nameplate capacity from 1980 through 2019, RectifHydPlus adds a hydrological control dataset that isolates the influence of historical water availability on generation. The new hydrological control dataset is suited to applications seeking to represent the capabilities of the contemporary fleet subject to historical interannual variability in climate. RectifHydPlus also includes a forty-year, daily-resolution, spill-adjusted water release time series for each dam, allowing users to aggregate generation estimates to the desired temporal resolution.

Hydroelectricity↗

DOE OSTI2022

Transverse oscillating bubble enhanced laser-driven betatron X-ray radiation generation

Abstract Ultrafast high-brightness X-ray pulses have proven invaluable for a broad range of research. Such pulses are typically generated via synchrotron emission from relativistic electron bunches using large-scale facilities. Recently, significantly more compact X-ray sources based on laser-wakefield accelerated (LWFA) electron beams have been demonstrated. In particular, laser-driven sources, where the radiation is generated by transverse oscillations of electrons within the plasma accelerator structure (so-called betatron oscillations) can generate highly-brilliant ultrashort X-ray pulses using a comparably simple setup. Here, we experimentally demonstrate a method to markedly enhance the parameters of LWFA-driven betatron X-ray emission in a proof-of-principle experiment. We show a significant increase in the number of generated photons by specifically manipulating the amplitude of the betatron oscillations by using our novel Transverse Oscillating Bubble Enhanced Betatron Radiation scheme. We realize this through an orchestrated evolution of the temporal laser pulse shape and the accelerating plasma structure. This leads to controlled off-axis injection of electrons that perform large-amplitude collective transverse betatron oscillations, resulting in increased radiation emission. Our concept holds the promise for a method to optimize the X-ray parameters for specific applications, such as time-resolved investigations with spatial and temporal atomic resolution or advanced high-resolution imaging modalities, and the generation of X-ray beams with even higher peak and average brightness.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design

Abstract Porous biomaterials design for bone repair is still largely limited to regular structures (e.g. rod-based lattices), due to their easy parameterization and high controllability. The capability of designing stochastic structure can redefine the boundary of our explorable structure–property space for synthesizing next-generation biomaterials. We hereby propose a convolutional neural network (CNN) approach for efficient generation and design of spinodal structure—an intriguing structure with stochastic yet interconnected, smooth, and constant pore channel conducive to bio-transport. Our CNN-based approach simultaneously possesses the tremendous flexibility of physics-based model in generating various spinodal structures (e.g. periodic, anisotropic, gradient, and arbitrarily large ones) and comparable computational efficiency to mathematical approximation model. We thus successfully design spinodal bone structures with target anisotropic elasticity via high-throughput screening, and directly generate large spinodal orthopedic implants with desired gradient porosity. This work significantly advances stochastic biomaterials development by offering an optimal solution to spinodal structure generation and design.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. Overall, we present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

A new portable random number generator wrapper library

Random number generator is an important component of many scientific projects. Many projects are written using programming models (like OpenMP and SYCL) to target different architectures. However, some programming models do not provide a random number generator. In this work, we introduce our random number generator wrapper. It is a header-only library that supports three distributions of random numbers: uniform, normal, and poisson. On the GPU backend, it wraps the cuRAND and rocRAND library, and supports various random number engines. It also wraps random123, a counterbased random number generator, on both CPU and GPU. With this library, we can generate random numbers with a few lines of code and target both GPU and multi-thread CPU with the same code. We also investigate the performance and scalability of this wrapper on different architectures with different engines and the number of cores.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Biermann battery magnetic fields in ICF capsules: Total magnetic flux generation

This paper focuses on the process of magnetic flux generation in inertial confinement fusion implosions. Hot-spots are shown to be dominated by fields generated during stagnation when the temperature and density gradients are largest. A scaling of hot-spot magnetic flux is derived and compared with simulations, revealing that perturbations with both larger amplitudes and higher mode numbers generate more magnetic flux. Furthermore, the model allows for greater understanding of which target designs will be susceptible to magnetohydrodynamic effects. For example, the model can be used to ascertain the time when most magnetic flux is generated. If generation is weighted more toward early times, then more high-mode magnetic field loops will be present. A hot-spot with no high-mode perturbations at time of peak neutron production can still contain significant magnetic flux on those scales. By assuming that magnetic flux is deposited at the hot-spot edge by Nernst advection, the model can be used to post-process radiation-hydrodynamics data to estimate magnetic field strengths and magnetizations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗