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DOE OSTI2023

Metal hydride composition-derived parameters as machine learning features for material design and H 2 storage

Though hydrogen is a promising energy carrier for a green future, many challenges persist. One is the difficulty in engineering storage solutions, with metal hydrides being a leading contender among solid-state strategies. To facilitate efficient searching of candidate materials, ridge regression, simple decision trees, random forest ensembles, and gradient boosting ensembles were employed to predict the energy of formation, with the random forest ensemble resulting in the lowest test set error. First, two public databases, Materials Project and HydPark, were searched for metal hydrides. Feature engineering was performed before the models were developed, resulting in electronegativity, density, atomic density, d-character, f-character, band gap, hydrogen weight fraction, magnetization, temperature, and pressure being retained. The models were then benchmarked by the lowest test error before a random forest ensemble was used to populate entries missing energy of formation. Furthermore, all were then scored by hydrogen storage capacity and energy of formation suitability. Readily available features including several derived from only the chemical formula which were found to be highly predictive. and so are promising for high-throughput screening of arbitrary novel hydride formulations and blends for thermodynamic feasibility.

25 ENERGY STORAGE↗

DOE OSTI2016

Adjoint-based sensitivity analysis for high-energy density radiative transfer using flux-limited diffusion

Uncertainty quantification and sensitivity analyses are a vital component for predictive modeling in the sciences and engineering. The adjoint approach to sensitivity analysis requires solving a primary system of equations and a mathematically related set of adjoint equations. The information contained in the equations can be combined to produce sensitivity information in a computationally efficient manner. Here in this work, sensitivity analyses are performed on systems described by flux-limited radiative diffusion using the adjoint approach. The sensitivities computed are shown to agree with standard perturbation theory and require significantly less computational time. The adjoint approach saves the computational cost of one forward solve per sensitivity, making the method attractive when multiple sensitivities are of interest.

42 ENGINEERING↗

DOE OSTI2019

Detecting control system misbehavior by fingerprinting programmable logic controller functionality

In recent years, attacks such as the Stuxnet malware have demonstrated that cyberattacks against control systems cause extensive damage. These attacks can result in physical damage to the networked systems under their control. In this paper, we discuss our approach for detecting such attacks by distinguishing between programs running on a programmable logic controller (PLC) without having to monitor communications. Using power signatures generated by an attached, high-frequency power measurement device, we can identify what a PLC is doing and when an attack may have altered what the PLC should be doing. To accomplish this, we generated labeled data for testing our methods and applied feature engineering techniques and machine learning models. The results of this research demonstrate that Random Forests and Convolutional Neural Networks classify programs with up to 98% accuracy for major program differences and 84% accuracy for minor differences. Our results can be used for both online and offline applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Probabilistic evolution of stochastic dynamical systems: A meso-scale perspective

Stochastic dynamical systems arise naturally across nearly all areas of science and engineering. Typically, a dynamical system model is based on some prior knowledge about the underlying dynamics of interest in which probabilistic features are used to quantify and propagate uncertainties associated with the initial conditions, external excitations, etc. From a probabilistic modeling standing point, two broad classes of methods exist, i.e. macro-scale methods and micro-scale methods. Classically, macro-scale methods such as statistical moments-based strategies are usually too coarse to capture the multi-mode shape or tails of a non-Gaussian distribution. Micro-scale methods such as random samples-based approaches, on the other hand, become computationally very challenging in dealing with high-dimensional stochastic systems. In view of these potential limitations, a meso-scale scheme is proposed here that utilizes a meso-scale statistical structure to describe the dynamical evolution from a probabilistic perspective. The significance of this statistical structure is twofold. First, it can be tailored to any arbitrary random space. Second, it not only maintains the probability evolution around sample trajectories but also requires fewer meso-scale components than the micro-scale samples. To demonstrate the efficacy of the proposed meso-scale scheme, a set of examples of increasing complexity are provided. Connections to the benchmark stochastic models as conservative and Markov models along with practical implementation guidelines are presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Accelerating room air conditioner efficiency in India: Grid, economic, and policy implications through 2035

India is poised for a rapid surge in space cooling demand, driven by rising incomes, urbanization, and intensifying heat. Between 2025 and 2035, the country is expected to add 130–150 million new room air conditioners (ACs). If Minimum Energy Performance Standards (MEPS) continue to improve at the historical rate of 2–3 % annually, room ACs alone could contribute over 180 GW to peak electricity demand by 2035-nearly 30 % of the projected national total. This study evaluates the impact of an accelerated MEPS trajectory, proposing to raise the 1-star threshold to ISEER 5.0 by 2027, ISEER 6.3 by 2030, and ISEER 7.4 by 2033. Drawing on engineering cost analysis, stock turnover modeling, and retail pricing data, we find that this pathway could reduce peak demand by over 60 GW, save 118 TWh of electricity annually, avoid 49 MtCO₂ of electricity-related emissions per year, avert ₹7.5 trillion (∼US$85 billion) in power system investments, and yield ₹0.7–2.3 trillion (∼US$8–26 billion) in net consumer savings by 2035. Contrary to affordability concerns, empirical trends show that higher efficiency does not increase AC prices. These results highlight the value of ambitious MEPS as a cost-effective strategy for improving grid reliability, reducing emissions, and advancing consumer welfare in emerging economies.

Abhyankar, Nikit↗

DOE OSTI2024

Revealing the three-dimensional arrangement of polar topology in nanoparticles

In the early 2000s, low dimensional ferroelectric systems were predicted to have topologically nontrivial polar structures, such as vortices or skyrmions, depending on mechanical or electrical boundary conditions. A few variants of these structures have been experimentally observed in thin film model systems, where they are engineered by balancing electrostatic charge and elastic distortion energies. However, the measurement and classification of topological textures for general ferroelectric nanostructures have remained elusive, as it requires mapping the local polarization at the atomic scale in three dimensions. Here we unveil topological polar structures in ferroelectric BaTiO3 nanoparticles via atomic electron tomography, which enables us to reconstruct the full three-dimensional arrangement of cation atoms at an individual atom level. Our three-dimensional polarization maps reveal clear topological orderings, along with evidence of size-dependent topological transitions from a single vortex structure to multiple vortices, consistent with theoretical predictions. The discovery of the predicted topological polar ordering in nanoscale ferroelectrics, independent of epitaxial strain, widens the research perspective and offers potential for practical applications utilizing contact-free switchable toroidal moments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Thin film photoelectrodes for solar water splitting

Photoelectrochemical (PEC) water splitting has been intensively studied in the past decades as a promising method for large-scale solar energy storage. Among the various issues that limit the progress of this field, the lack of photoelectrode materials with suitable properties in all aspects of light absorption, charge separation and transport, and charge transfer is a key challenge, which has attracted tremendous research attention. A large variety of compositions, in different forms, have been tested. This review aims to summarize efforts in this area, with a focus on materials-related considerations. Issues discussed by this review include synthesis, optoelectronic properties, charge behaviors and catalysis. In the recognition that thin-film materials are representative model systems for the study of these issues, we elected to focus on this form, so as to provide a concise and coherent account on the different strategies that have been proposed and tested. Because practical implementation is of paramount importance to the eventual realization of using solar fuel for solar energy storage, we pay particular attention to strategies proposed to address the stability and catalytic issues, which are two key factors limiting the implementation of efficient photoelectrode materials. To keep the overall discussion focused, all discussions were presented within the context of water splitting reactions. Finally, how the thin-film systems may be applied for fundamental studies of the water splitting chemical mechanisms and how to use the model system to test device engineering design strategies are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

A coordination-based model for transition metal alloy nanoparticles

We present a simple approach for predicting the relative energies of bimetallic nanoparticles spanning a wide-ranging combinatorial space, using only the identity and nearest-neighbor coordination number of individual metal atoms as independent parameters. By performing straightforward metal atom adsorption calculations on surface slab models, we parameterize expressions for the energy of metal atoms as a function of their coordination number in 21 bimetallic pairings of fcc metals. Here, we rigorously establish the transferability of our model by predicting relative energies of a series of nanoparticles across a large number of morphologies, sizes, atomic compositions, and arrangements. The model is particularly accurate in predicting atomic rearrangements at or near the metal surfaces, which is essential for its potential applications when studying segregation phenomena or dynamic processes in heterogeneous catalysis. By rapidly forecasting site stabilities with atomic specificity across generic structural and compositional features, our model is able to reverse engineer thermodynamically feasible motifs of active sites in bimetallic nanoparticles through robust property ⇔ structure relations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2015

Tunable thermoelectric transport in nanomeshes via elastic strain engineering

Recent experimental explorations of silicon nanomeshes have shown that the unique metastructures exhibit reduced thermal conductivity while preserving bulk electrical conductivity via feature sizes between relevant phonon and electron mean free paths, aiding in the continued promise that nanometer-scale engineering may further enhance thermoelectric behavior. Here, we introduce a strategy for tuning thermoelectric transport phenomena in semiconductor nanomeshes via heterogeneous elastic strain engineering, using silicon as a model material for demonstration of the concept. By combining analytical models for electron mobility in uniformly stressed silicon with finite element analysis of strained silicon nanomeshes in a lumped physical model, we show that the nonuniform and multiaxial strain fields defined by the nanomesh geometry give rise to spatially varying band shifts and warping, which in aggregate accelerate electron transport along directions of applied stress. This allows for global electrical conductivity and Seebeck enhancements beyond those of homogenous samples under equivalent far-field stresses, ultimately increasing thermoelectric power factor nearly 50% over unstrained samples. The proposed concept and structures—generic to a wide class of materials with large dynamic ranges of elastic strain in nanoscale volumes—may enable a new pathway for active and tunable control of transport properties relevant to waste heat scavenging and thermal management.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

From vertices to vortices in magnetic nanoislands

We report recent studies in magnetic nanoarrays show that a variety of complex magnetic states and textures emerge as a function of a single magnetic nanoisland's aspect ratio. We propose a model that, in addition to fitting experiments, predicts magnetic states with continuous symmetry at particular aspect ratios and reveals a duality between vortex and vertex states. Our model opens new means of engineering novel types of artificial spin systems, and their application to complex magnetic textures in devices and computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

A search for optical variability of type 2 quasars in SDSS stripe 82

Hundreds of Type 2 quasars have been identified in Sloan Digital Sky Survey (SDSS) data, and there is substantial evidence that they are generally galaxies with highly obscured central engines, in accord with unified models for active galactic nuclei (AGNs). A straightforward expectation of unified models is that highly obscured Type 2 AGNs should show little or no optical variability on timescales of days to years. As a test of this prediction, we have carried out a search for variability in Type 2 quasars in SDSS Stripe 82 using difference-imaging photometry. Starting with the Type 2 AGN catalogs of Zakamska et al. and Reyes et al., we find evidence of significant g-band variability in 17 out of 173 objects for which light curves could be measured from the Stripe 82 data. To determine the nature of this variability, we obtained new Keck spectropolarimetry observations for seven of these variable AGNs. The Keck data show that these objects have low continuum polarizations (p ≲ 1% in most cases) and all seven have broad Hα and/or Mg II emission lines in their total (unpolarized) spectra, indicating that they should actually be classified as Type 1 AGNs. We conclude that the primary reason variability is found in the SDSS-selected Type 2 AGN samples is that these samples contain a small fraction of Type 1 AGNs as contaminants, and it is not necessary to invoke more exotic possible explanations such as a population of 'naked' or unobscured Type 2 quasars. Aside from misclassified Type 1 objects, the Type 2 quasars do not generally show detectable optical variability over the duration of the Stripe 82 survey.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

GRAVITATIONAL WAVES OF JET PRECESSION IN GAMMA-RAY BURSTS

The physical nature of gamma-ray bursts (GRBs) is believed to involve an ultra-relativistic jet. The observed complex structure of light curves motivates the idea of jet precession. In this work, we study the gravitational waves of jet precession based on neutrino-dominated accretion disks around black holes, which may account for the central engine of GRBs. In our model, the jet and the inner part of the disk may precess along with the black hole, which is driven by the outer part of the disk. Gravitational waves are therefore expected to be significant from this black-hole-inner-disk precession system. By comparing our numerical results with the sensitivity of some detectors, we find that it is possible for DECIGO and BBO to detect such gravitational waves, particularly for GRBs in the Local Group.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Numerical investigation of alpha particle confinement under the perturbation of neoclassical tearing modes and toroidal field ripple in CFETR

The confinement of alpha particles in burning plasma is a key issue in fusion reactor design, including particle interaction with instabilities. Here, we include two topics: the effect of neoclassical tearing modes (NTMs) and toroidal field ripple on alpha particle loss, and the assessment of particle redistribution under an NTM with a reduced model. We consider Chinese fusion engineering test reactor parameters, the alpha particle distribution given by TRANSP/NUBEAM and the NTM perturbation function given by the initial value code TM1. We show that the synergistic effect of the NTM and ripple is negligible; the particle loss fraction does not change with increasing NTM amplitude. Only passing particles are affected by the mode particle resonance, producing profile flattening but no increased loss because only trapped particles are influenced by ripple. To study alpha particle profile flattening, the work adopts an innovative method of phase vector rotation to determine regions of good and broken Kolmogorov–Arnold–Moser surfaces and equilibrates the particle density according to local stochasticity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Fixation of CO 2 and CO on a diverse range of carbohydrates using anaerobic, non-photosynthetic mixotrophy

Biological CO 2 fixation is an important technology that can assist in combating climate change. Here, we show an approach called anaerobic, non-photosynthetic mixotrophy can result in net CO 2 fixation when using a reduced feedstock. This approach uses microbes called acetogens that are capable of concurrent utilization of both organic and inorganic substrates. In this study, we investigated the substrate utilization of 17 different acetogens, both mesophilic and thermophilic, on a variety of different carbohydrates and gases. Compared to most model acetogen strains, several non-model mesophilic strains displayed greater substrate flexibility, including the ability to utilize disaccharides, glycerol and an oligosaccharide, and growth rates. Three of these non-model strains ( Blautia producta , Clostridium scatologenes and Thermoanaerobacter kivui ) were chosen for further characterization, under a variety of conditions including H 2 - or syngas-fed sugar fermentations and a CO 2 -fed glycerol fermentation. In all cases, CO 2 was fixed and carbon yields approached 100%. Finally, the model acetogen C. ljungdahlii was engineered to utilize glucose, a non-preferred sugar, while maintaining mixotrophic behavior. This work demonstrates the flexibility and robustness of anaerobic, non-photosynthetic mixotrophy as a technology to help reduce CO 2 emissions.

09 BIOMASS FUELS↗

DOE OSTI2025

Emergent Inductance from Chiral Orbital Currents in a Bulk Ferrimagnet

Here, we report the discovery of a new form of inductance in the bulk ferrimagnet Mn 3 ⁢Si 2 ⁢Te 6 , which features strong spin-orbit coupling, large magnetic anisotropy, and pronounced magnetoelastic interactions. Below its Curie temperature (𝑇 𝐶 ≈ 78 K), Mn 3 ⁢Si 2 ⁢Te 6 hosts chiral orbital currents (COC) that circulate within the crystal lattice and give rise to collective electronic behavior [Control of chiral orbital currents in a colossal magnetoresistance material, Nature (London) 611, 467 (2022).]. By applying a magnetic field along the hard 𝑐 axis and driving the system with low-frequency currents, we uncover a giant inductive response up to millhenry scale, originating from first-order reconfigurations of COC domains. These domains act as coherent mesoscopic inductive elements that resist reversal upon current reduction, producing a large electromotive force and sharply increasing voltage. This emergent inductance defies classical models, occurs without superconductivity or engineered nanostructures, and opens a new frontier in orbital-based quantum functionality and device concepts.

complex materials↗

DOE OSTI2018

IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks

Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successful in computer vision and natural language applications. Designing optimal network architectures from a principled or rational approach however has been less than successful, with the best successful approaches utilizing an additional machine learning algorithm to tune the network hyperparameters. However, in many technical fields, there exist established domain knowledge and understanding about the subject matter. In this work, we develop a novel furcated neural network architecture that utilizes domain knowledge as high-level design principles of the network. We demonstrate proof-of-concept by developing IL-Net, a furcated network for predicting the properties of ionic liquids, which is a class of complex multi-chemicals entities. Compared to existing state-of-the-art approaches, we show that furcated networks can improve model accuracy by approximately 20-35%, without using additional labeled data. Lastly, we distill two key design principles for furcated networks that can be adapted to other domains.

Sakloth, Khushmeen↗

DOE OSTI2018

Detailed Chemical Kinetics Based Simulation of Detonation-Containing Flows

Reliable and robust simulations of detonations in inhomogeneous and turbulent environments are of direct importance in the design of rotating detonation engines (RDEs). In particular, computational models will be especially useful in designing and optimizing discrete injectors that introduce fuel and air separately into the detonation chamber, but ensure appropriate level of mixing to sustain detonations but minimize backflow of detonation products and pressure waves into the feed plenums. Since the structure of detonations itself is non-ideal, models have to include a detailed description of this reacting flow in order to be predictive in nature. Here, a highly-scalable open source based solver has been developed for complex detonating flows such that a) the detonation processes are described using detailed chemical kinetics, b) the method is computationally efficient through the introduction of adaptive mesh refinement, and c) the solver can handle complex geometries of relevance to RDE design. Grid convergence of key metrics for detonations is evaluated using canonical flows. Further, the importance of the use of detailed chemical kinetics is illustrated by extracting the composition structure behind a two-dimensional detonation front. Finally, simulations of a practical RDE configuration are used to demonstrate the applicability of this solver to analyzing geometries. The simulation captures the general trends of the experiment well. It is found that the detonation occurs under partially-premixed conditions. Propagation of pressure waves to the injection system is observed which could influence flow behavior in the oxidizer plenum.

03 NATURAL GAS↗

DOE OSTI2020

SYSTEMATIC MULTI-INDEX VALIDATIONS OF SIDI ENGINE FLOW FIELD LES COMPUTATIONS USING CRANK ANGLE-RESOLVED PIV MEASUREMENTS

In-cylinder flow fields make significant impacts on the fuel atomization, fuel mixture formation, and combustion process in spark ignition direct injection (SIDI) engines. In recent years, model-based simulation approaches are preferred in regard to investigating the transient in-cylinder flow field characteristics. Most commonly, the simulation models are validated using single representative flow field at a typical crank angle measured by particle image velocimetry (PIV). However, it provides only limited knowledge about the flow field which is highly three-dimensional and of transient nature. In this study, crank angle-resolved Ply measurements are conducted on three distinct planes inside the cylinder to capture the transient process of flow field characteristics which vary with the crank angle. These three planes consist of one tumble plane through the spark plug tip, one tumble plane along two intake ports, and one swirl plane at 30 mm below the cylinder head. Large eddy simulation (LES) is employed for the numerical computations using the CONVERGE codes. On the basis of large datasets for both temporal and spatial domains, a multi-index systematic validation approach is conducted. Crank angle-resolved calculations of global indices and local indices are implemented using the flow fields velocity data obtained from both PIV and LES on select planes. Global indices reveal the trends in similarities of different crank angle degrees and locations, while local indices give the detail comparison results. In summary, with the systematic multi-index validation approach, the crucial crank angle degrees and locations for model verification will be detected. Furthermore, the corresponding critical flow features are analyzed. Practical guideline of flow field validation is proposed.

Crank angle-resolved PIV↗

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