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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improved machine learning algorithm for predicting ground state properties

Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only $\mathcal{O}$(log(n)) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $\mathcal{O}$(n c ) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as $\mathcal{O}$(n log n) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Optical neural engine for solving scientific partial differential equations

Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.

Tang, Yingheng (ORCID:0009000153622546)↗
DOE OSTI2025

Global quantification of the dispersion effect with POLDER satellite data

Increased aerosols can modify the shape of the cloud Particle Size Distribution (PSD), thereby influencing the radiative properties of clouds, known as the Dispersion Effect (DE). However, a global, observation-based quantification of its impact on Aerosol-Cloud Interactions (ACI) is lacking, leading to DE being typically ignored in satellite-based estimates of ACI forcing. Here we propose a physics-based method that combines polarimetric satellite data on cloud PSD to achieve global observational quantification of DE’s impact on ACI in liquid-phase stratiform clouds. Globally, DE offsets ACI changes induced by droplet number concentration variation and liquid water path adjustment by 7% and −1.4%, respectively. Furthermore, a parameterization based on the global dataset of PSD shape parameters is developed to improve DE estimation in large-scale models. Both the quantification and parameterization enhance our understanding of DE and facilitate the inclusion of this non-negligible impact of DE on ACI in estimating aerosol climate forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

High yield production of 3-hydroxypropionic acid using Issatchenkia orientalis

Biomanufacturing provides a more sustainable alternative to fossil-based chemical manufacturing. 3-Hydroxypropionic acid (3HP) is a top Department of Energy value-added chemical and precursor to bioplastics, yet cost-effective microbial production remains elusive. Here, we establish the acid-tolerant yeast Issatchenkia orientalis as a robust host for low-pH 3HP biosynthesis. Genome-scale modeling identifies the β-alanine pathway as optimal, offering the highest theoretical yield and lowest oxygen requirement. Thermodynamic analysis confirms its favorability under acidic conditions. Using sequence similarity network analysis, we discover highly active aspartate 1-decarboxylase (PAND), β-alanine-pyruvate aminotransferase (BAPAT), and 3HP dehydrogenase (YDFG), which significantly improve the pathway efficiency. Next, to further elevate the production, pathway optimization through multi-copy PAND integration, byproduct elimination (knockouts of pyruvate decarboxylase and glycerol-3-phosphate dehydrogenase), and reinforcement of aspartate flux by overexpression of pyruvate carboxylase and aspartate amino transferase improves the titer to 29 g/L in shake flasks. Fed-batch fermentation at pH 4 with low-cost corn steep liquor medium further increases the production to 92 g/L with 0.7 g/g yield and 0.55 g/L/h productivity. Techno-economic analysis indicates that such performance could potentially enable a financially viable process for sustainable acrylic acid production. This work establishes I. orientalis as a next-generation platform for cost-effective 3HP production and paves the way toward industrial commercialization.

Biotechnology↗
DOE OSTI2020

Graph theory approach to determine configurations of multidentate and high coverage adsorbates for heterogeneous catalysis

Abstract Heterogeneous catalysts constitute a crucial component of many industrial processes, and to gain an understanding of the atomic-scale features of such catalysts, ab initio density functional theory is widely employed. Recently, growing computational power has permitted the extension of such studies to complex reaction networks involving either high adsorbate coverages or multidentate adsorbates, which bind to the surface through multiple atoms. Describing all possible adsorbate configurations for such systems, however, is often not possible based on chemical intuition alone. To systematically treat such complexities, we present a generalized Python-based graph theory approach to convert atomic scale models into undirected graph representations. These representations, when combined with workflows such as evolutionary algorithms, can systematically generate high coverage adsorbate models and classify unique minimum energy multidentate adsorbate configurations for surfaces of low symmetry, including multi-elemental alloy surfaces, steps, and kinks. Two case studies are presented which demonstrate these capabilities; first, an analysis of a coverage-dependent phase diagram of absorbate NO on the Pt 3 Sn(111) terrace surface, and second, an investigation of adsorption energies, together with identifying unique minimum energy configurations, for the reaction intermediate propyne (CHCCH 3 *) adsorbed on a PdIn(021) step surface. The evolutionary algorithm approach reproduces high coverage configurations of NO on Pt 3 Sn(111) using only 15% of the number of simulations required for a brute force approach. Furthermore, the screening of potentially hundreds of multidentate adsorbates is shown to be possible without human intervention. The strategy presented is quite general and can be applied to a spectrum of complex atomic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-scale investigation of short-range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys

Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of short-range ordering (SRO) on dislocation glide in the MoNbTi and TaNbTi RMPEAs using a multi-scale modeling approach. Monte carlo/molecular dynamics simulations with a moment tensor potential show that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies (USFEs). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs and stress required for dislocation glide. The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, amount of hardening. Finally, we show how the morphology of an expanding dislocation loop is affected by the applied stress.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Assessment of chemo-mechanical impacts of CO 2 sequestration on the caprock formation in Farnsworth oil field, Texas

This study evaluates the chemo-mechanical influence of injected CO 2 on the Morrow B sandstone reservoir and the upper Morrow shale caprock utilizing data from the inverted 5-spot pattern centered on Well 13-10A within the Farnsworth unit (FWU). This study also seeks to evaluate the integrity of the caprock and the long-term CO 2 storage capability of the FWU. The inverted 5-spot pattern was extracted from the field-scale model and tuned with the available field observed data before the modeling work. Two coupled numerical simulation models were utilized to continue the study. First, a coupled hydro-geochemical model was constructed to simulate the dissolution and precipitation of formation minerals by modeling three intra-aqueous and six mineral reactions. In addition, a coupled hydro-geomechanical model was constructed and employed to study the effects of stress changes on the caprock’s porosity, permeability, and ground displacement. The Mohr–Coulomb circle and failure envelope were used to determine caprock failure. In this work, the CO 2 -WAG injection is followed by the historical field-observed strategy. During the forecasting period, a Water Alternating Gas (WAG) injection ratio of 1:3 was utilized with a baseline bottom-hole pressure constraint of 5500 psi for 20 years. A post-injection period of 1000 years was simulated to monitor the CO 2 plume and its effects on the CO 2 storage reservoir and caprock integrity. The simulation results indicated that the impacts of the geochemical reactions on the porosity of the caprock were insignificant as it experienced a decrease of about 0.0003% at the end of the 1000-year post-injection monitoring. On the other hand, the maximum stress-induced porosity change was about a 1.4% increase, resulting in about 4% in permeability change. It was estimated that about 3.3% of the sequestered CO 2 in the formation interacted with the caprock. Despite these petrophysical property alterations and CO 2 interactions in the caprock, the caprock still maintained its elastic properties and was determined to be far from its failure.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2025

Dynamic soil columns simulate Arctic redox biogeochemistry and carbon release during changes in water saturation

Thawing Arctic permafrost can induce hydrologic change and alter redox conditions, shifting the balance of soil organic matter (SOM) decomposition. There remains uncertainty about how soil saturation and redox transitions impact dissolved and gas phase carbon fluxes, and efforts to link hydrobiogeochemical processes to ecosystem-scale models are limited. This study evaluates SOM decomposition of Arctic tundra soils using column experiments, water chemistry measurements, microbial community analysis, and a PFLOTRAN reactive transport model. Soil columns from a thermokarst channel (TC) and an upland tundra (UC) were exposed to cycles of saturation and drainage, which controlled carbon emissions. During saturation, an outflow of dissolved organic carbon from the UC soil correlated with elevated reduced iron and decreased pH; during drainage, UC carbon dioxide fluxes were 70% higher than TC fluxes. Intermittent methane release was observed for TC, consistent with higher methanogen abundance. Slower drainage in the TC soil correlated with more subtle biogeochemical changes. PFLOTRAN simulations captured experimental trends in soil carbon fluxes, oxygen concentrations, and water contents. The model was then used to evaluate additional soil water drainage rates. This study emphasizes the importance of considering hydrologic change when evaluating and simulating SOM decomposition in dynamic Arctic tundra environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. Furthermore, we discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

ZTF SN Ia DR2: Improved SN Ia colors through expanded dimensionality with SALT3+

Context. Type Ia supernovae (SNe Ia) are a key probe in modern cosmology, as they can be used to measure luminosity distances at gigaparsec scales. Models of their light curves are used to project heterogeneous observed data onto a common basis for analysis. Aims. The SALT model currently used for SN Ia cosmology describes SNe as having two sources of variability, accounted for by a color parameter c , and a “stretch” parameter x 1 . We extend the model to include an additional parameter we label x 2 , to investigate the cosmological impact of currently unaddressed light-curve variability. Methods. We constructed a new SALT model, that we dub “SALT3+”. This model was trained by an improved version of the SALTshaker code, using training data combining a selection of the second data release of cosmological SNe Ia from the Zwicky Transient Facility and the existing SALT3 training compilation. Results. We find additional, coherent variability in supernova light curves beyond SALT3. Most of this variation can be described as phase-dependent variation in g − r and r − i color curves, correlated with a boost in the height of the secondary maximum in i -band. These behaviors correlate with spectral differences, particularly in line velocity. We find that fits with the existing SALT3 model tend to address this excess variation with the color parameter, leading to less informative measurements of supernova color. We find that neglecting the new parameter in light-curve fits leads to a trend in Hubble residuals with x 2 of 0.039 ± 0.005 mag, representing a potential systematic uncertainty. However, we find no evidence of a bias in current cosmological measurements. Conclusions. We conclude that extended SN Ia light-curve models promise mild improvement in the accuracy of color measurements, and corresponding cosmological precision. However, models with more parameters are unlikely to substantially affect current cosmological results.

Kenworthy, W. D. (ORCID:0000000251535983)↗
DOE OSTI2020

Non-dilute helium-related defect interactions in the near-surface region of plasma-exposed tungsten

We report a systematic energetic analysis of helium-related defect interactions that mediate helium (He) segregation on surfaces of plasma-exposed tungsten at different levels of He ion implantation. We focus on high He fluences that increase the He content in the plasma-exposed material well beyond the dilute limit of He concentration and employ atomic configurations generated by large-scale molecular dynamics simulations of He-implanted tungsten. We perform systematic molecular statics computations of cluster– defect interaction energetics in the highly defect-rich near-surface region of plasma-exposed tungsten for small mobile helium clusters as a function of the cluster distance from the surface. In this region, mobile clusters are also subjected to the stress fields generated by defects such as helium bubbles and other clusters, which govern cluster–defect interactions in addition to the cluster–surface interaction. Based on systematic investigation of individual cluster–defect interactions, we develop a mathematical framework to describe the interaction energy landscapes consisting of elastic interaction potential perturbations to finite-width square-well potentials, where the potential well accounts for cluster trapping by the defect at close range and subsequent coalescence and the perturbation potential is parameterized according to elastic inclusion theory. Superposition of all the relevant interaction potentials provides a comprehensive description of the interaction energy landscape that would be experienced by a small mobile cluster along its migration path toward the plasma-exposed surface at high He fluence. Such descriptions are particularly important for developing atomistically-informed, hierarchical multi-scale models of helium cluster dynamics in plasma-facing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scaling of laser-driven electron and proton acceleration as a function of laser pulse duration, energy, and intensity in the multi-picosecond regime

A scaling study of short-pulse laser-driven proton and electron acceleration was conducted as a function of pulse duration, laser energy, and laser intensity in the multi-picosecond (ps) regime (~0.8 ps–20 ps). Furthermore, maximum proton energies significantly greater than established scaling laws were observed, consistent with observations at other multi-ps laser facilities. In addition, maximum proton energies and electron temperatures in this regime were found to be strongly dependent on the laser pulse duration and preplasma conditions. A modified proton scaling model is presented that is able to better represent the accelerated proton characteristics in this multi-ps regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Ion irradiation and modification: The role of coupled electronic and nuclear energy dissipation and subsequent nonequilibrium processes in materials

Understanding material responses to energy deposition from energetic charged particles is important for defect engineering, ion-beam processing, ion-beam analysis and modification, geologic aging, space exploration, and nuclear applications. As an incident ion penetrates a solid, its energy is transferred to electrons and to atomic nuclei of the solid. Much of this electronic energy deposition is subsequently transferred to the atomic structure via electron–phonon (e–ph) coupling, leading to local inelastic thermal spikes in which energy dissipation is influenced by the local environment. In addition, intense ionization can lead to high densities of localized electronic excitations in wide-bandgap materials and ceramics that can affect defect dynamics and atomic mobility. Specifically, energy exchange between electrons and atomic nuclei, along with localized electronic excitations, can lead to substantial competitive (ionization-induced annealing), additive (both electronic and nuclear energy depositions contributing to damage production), and synergistic (more damage than the sums of separate damage processes) effects. Although nonmonotonic effects of the e–ph coupling strength and athermal processes are demonstrated for pre-existing defects and residual damage during ion–solid interactions, there is limited understanding of when such electronic effects must be considered in atomic-scale models of damage production and evolution in a broad variety of materials. Complex ceramics and chemically disordered solid solution alloys with different constituent elements allow a systematic evaluation of defect dynamics and irradiation performance with increasing complexity. Current knowledge regarding tuning of bonding characteristics and chemical disorder to control atomic-level dynamics is reviewed. Although a lack of fundamental understanding obstructs the advancement of reliable predictions for ion beam material modification, it highlights challenges and opens research opportunities. Insights into the complex electronic and atomic correlations with extreme energy deposition will strengthen our ability to design materials and predict ion-irradiation-induced damage in a radiation environment, and they may pave the way to better control fundamental processes and design new material functionalities for advanced technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Fuel convergence sensitivity in indirect drive implosions

In inertial confinement fusion experiments at the National Ignition Facility, a spherical shell of deuterium–tritium fuel is imploded in an attempt to reach the conditions needed for fusion, self-heating, and eventual ignition. Since theory and simulations indicate that ignition efficacy in 1D improves with increasing imploded fuel convergence ratio, it is useful to understand the sensitivity of the scale-invariant fuel convergence on all measurable or inferable 1D parameters. In this paper, we develop a simple isobaric and isentropic compression scaling model incorporating sensitivity to the in-flight adiabat inferred from shock strengths, to measured implosion velocity, and to known initial ablator and fuel aspect ratio and mass ratio. The model is first benchmarked to 1D implosion simulations spanning a variety of relevant implosion designs. We then use the model to compare compressibility trends across all existing indirect-drive layered implosion data from the facility spanning three ablators [CH, carbon (C), and Be], for which in-flight fuel adiabats varied from 1.6 to 5 by varying the number of drive shocks from 2 to 4, peak implosion velocities varied by 1.4×, capsule radii by 50%, and initial fuel aspect ratios by 1.4×. We find that the strength of the first shock is the dominant contributor setting the maximum fuel convergence. We also observe additional sensitivities to successive shock strengths and fuel aspect ratios that improve the agreement between the expected and measured compression for carbon and Be designs with adiabats above 3. A principal finding is that the adiabat 2.5 C-shell designs exhibit less convergence than CH-shell designs of similar inferred in-flight adiabat.

Landen, O. L. (ORCID:0000000214998217)↗
DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Energy localization efficiency in 1,3,5-trinitro-2,4,6-triaminobenzene pore collapse mechanisms

Atomistic and continuum scale modeling efforts have shown that the shock-induced collapse of porosity can occur via a wide range of mechanisms dependent on pore morphology, the shockwave pressure, and material properties. The mechanisms that occur under weaker shocks tend to be more efficient at localizing thermal energy but do not result in high, absolute temperatures or spatially large localizations compared to mechanisms found under strong shock conditions. However, the energetic material 1,3,5-trinitro-2,4,6-triaminobenzene (TATB) undergoes a wide range of collapse mechanisms that are not typical of similar materials, leaving the collapse mechanisms and the resultant energy localization from the collapse, i.e., hotspots, relatively uncharacterized. Therefore, we present the pore collapse simulations of cylindrical pores in TATB for a wide range of pore sizes and shock strengths that trigger viscoplastic collapses that occur almost entirely perpendicular to the shock direction for weak shocks and hydrodynamic-like collapses for strong shocks that do not break the strong hydrogen bonds of the TATB basal planes. The resulting hotspot temperature fields from these mechanisms follow trends that differ considerably from other energetic materials; hence, we compare them under normalized temperature values to assess the relative efficiency of each mechanism to localize energy. The local intra-molecular strain energy of the hotspots is also assessed to better understand the physical mechanisms behind the phenomena that lead to a latent potential energy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Origin of the injection-dependent emission blueshift and linewidth broadening of III-nitride light-emitting diodes

III-nitride light-emitting diodes (LEDs) exhibit an injection-dependent emission blueshift and linewidth broadening that is severely detrimental to their color purity. By using first-principles multi-scale modeling that accurately captures the competition between polarization-charge screening, phase-space filling, and many-body plasma renormalization, we explain the current-dependent spectral characteristics of polar III-nitride LEDs fabricated with state-of-the-art quantum wells. Our analysis uncovers a fundamental connection between carrier dynamics and the injection-dependent spectral characteristics of light-emitting materials. For example, polar III-nitride LEDs offer poor control over their injection-dependent color purity due to their poor hole transport and slow carrier-recombination dynamics, which forces them to operate at or near degenerate carrier densities. Designs that accelerate carrier recombination and transport and reduce the carrier density required to operate LEDs at a given current density lessen their injection-dependent wavelength shift and linewidth broadening.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗