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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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DOE OSTI2021

Progress toward favorable landscapes in quantum combinatorial optimization

The performance of variational quantum algorithms relies on the success of using quantum and classical computing resources in tandem. Here, we study how these quantum and classical components interrelate. In particular, we focus on algorithms for solving the combinatorial optimization problem MaxCut, and study how the structure of the classical optimization landscape relates to the quantum circuit used to evaluate the MaxCut objective function. In order to analytically characterize the impact of quantum features on the critical points of the landscape, we consider a family of quantum circuit ansätze composed of mutually commuting elements. We identify multiqubit operations as a key resource and show that overparameterization allows for obtaining favorable landscapes. Namely, we prove that an ansatz from this family containing exponentially many variational parameters yields a landscape free of local optima for generic graphs. However, we further prove that these ansätze do not offer superpolynomial advantages over purely classical MaxCut algorithms. Here, we then present a series of numerical experiments illustrating that noncommutativity and entanglement are important features for improving algorithm performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics

Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. Here we demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Anthropogenically driven climate and landscape change effects on inland water carbon dynamics: What have we learned and where are we going?

Inland waters serve as important hydrological connections between the terrestrial landscape and oceans but are often overlooked in global carbon (C) budgets and Earth System Models. Terrestrially derived C entering inland waters from the watershed can be transported to oceans but over 83% is either buried in sediments or emitted to the atmosphere before reaching oceans. Anthropogenic pressures such as climate and landscape changes are altering the magnitude of these C fluxes in inland waters. Here, we synthesize the most recent estimates of C fluxes and the differential contributions across inland waterbody types (rivers, streams, lakes, reservoirs, and ponds), including recent measurements that incorporate improved sampling methods, small waterbodies, and dried areas. Across all inland waters, we report a global C emission estimate of 4.40 Pg C/year (95% confidence interval: 3.95–4.85 Pg C/year), representing a 13% increase from the most recent estimate. In this work, we also review the mechanisms by which the most globally widespread anthropogenically driven climate and landscape changes influence inland water C fluxes. The majority of these drivers are expected to influence terrestrial C inputs to inland waters due to alterations in terrestrial C quality and quantity, hydrological pathways, and biogeochemical processing. We recommend four research priorities for the future study of anthropogenic alterations to inland water C fluxes: (1) before-and-after measurements of C fluxes associated with climate change events and landscape changes, (2) better quantification of C input from land, (3) improved assessment of spatial coverage and contributions of small inland waterbodies to C fluxes, and (4) integration of dried and drawdown areas to global C flux estimates. Improved measurements of inland water C fluxes and quantification of uncertainty in these estimates will be vital to understanding both terrestrial C losses and the “moving target” of inland water C emissions in response to rapid and complex anthropogenic pressures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Interactive Mobility Data Landscape

This is the companion repository to NREL's Interactive Mobility Landscape App (SEE: DOE Code ID 61291. NREL SWR-21-10). The Interactive Mobility Data Landscape is a project intended as a map through the previously uncharted, growing mobility data science field. This attempts to categorize most of the data sources, specifications, and tools in the field of mobility. It has been built by the National Renewable Energy Laboratory's (NREL) Mobility, Behavior, and Advanced Powertrains (MBAP) research group. The software for the interactive landscape is located at https://github.com/NREL/mobility_landscapeapp This repo includes all of the data and images specific to the Interactive Mobility Data Landscape.

Shankari, Kalyanaraman↗
DOE OSTI2020

Interactive Mobility Landscape App

This is the companion repository to NREL's Interactive Mobility Data Landscape (SEE: DOE Code ID 61284. NREL SWR-21-09). The landscapeapp is an upstream NPM module that supports building interactive landscape websites such as the CNCF Cloud Native Landscape (source) and the LF Artificial Intelligence Landscape (source). The application is managed by Dan Kohn of CNCF and is under active development by Andrey Kozlov (who did most of the development to date, 9/13/2021) and Jordi Noguera.

Shankari, Kalyanaraman↗
DOE OSTI2024

Weak Scale Supersymmetry Emergent from the String Landscape

Superstring flux compactifications can stabilize all moduli while leading to an enormous number of vacua solutions, each leading to different 4−d laws of physics. While the string landscape provides at present the only plausible explanation for the size of the cosmological constant, it may also predict the form of weak scale supersymmetry which is expected to emerge. Rather general arguments suggest a power-law draw to large soft terms, but these are subject to an anthropic selection of a not-too-large value for the weak scale. The combined selection allows one to compute relative probabilities for the emergence of supersymmetric models from the landscape. Models with weak scale naturalness appear most likely to emerge since they have the largest parameter space on the landscape. For finetuned models such as high-scale SUSY or split SUSY, the required weak scale finetuning shrinks their parameter space to tiny volumes, making them much less likely to appear compared to natural models. Probability distributions for sparticle and Higgs masses from natural models show a preference for Higgs mass mh∼125 GeV, with sparticles typically beyond the present LHC limits, in accord with data. From these considerations, we briefly describe how natural SUSY is expected to be revealed at future LHC upgrades. This article is a contribution to the Special Edition of the journal Entropy, honoring Paul Frampton on his 80th birthday.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Delayed fire mortality has long‐term ecological effects across the Southern Appalachian landscape

Abstract Fire is a critical ecological process to the forests of the Southern Appalachians. Where fire was excluded from forest types that historically burned frequently, unanticipated changes can occur when fire is reintroduced. For example, the development of new fuel characteristics can change the patterns of fire mortality and associated ecological responses. To test the fire effects of delayed fire mortality (mortality initiated by fire that occurs subsequent to the fire year) in the Southern Appalachians, USA, we developed a fire‐effects model using both field studies and remote sensing. We then simulated these effects at a landscape scale to estimate broader ecological effects. Fire‐effects models that accounted for delayed mortality increased landscape biomass removed annually (~23%) and increased the number of sites with high light conditions (leaf area index < 4) when compared to simulations that only account for immediate mortality. While delayed mortality occurred across species and age classes, it was especially prevalent among older trees (>100 years old) and fire‐resistant species ( Quercus spp.). Overall, regeneration (trees <20 years old) changed very little, even with the inclusion of delayed mortality. This evidence suggests that, even when accounting for delayed mortality, individual fires are unlikely to shift the landscape composition toward the conditions of forests prior to fire exclusion and may even increase mesophication long term due to the loss of overstory dominant xeric trees.

Robbins, Zachary J.↗
DOE OSTI2022

Exploiting the Local Parabolic Landscapes of Adversarial Losses to Accelerate Black-Box Adversarial Attack

Existing black-box adversarial attacks on image classifiers update the perturbation at each iteration from only a small number of queries of the loss function. Since the queries contain very limited information about the loss, black-box methods usually require much more queries than white-box methods. We propose to improve the query efficiency of black-box methods by exploiting the smoothness of the local loss landscape. However, many adversarial losses are not locally smooth with respect to pixel perturbations. To resolve this issue, our first contribution is to theoretically and experimentally justify that the adversarial losses of many standard and robust image classifiers behave like parabolas with respect to perturbations in the Fourier domain. Our second contribution is to exploit the parabolic landscape to build a quadratic approximation of the loss around the current state, and use this approximation to interpolate the loss value as well as update the perturbation without additional queries. Since the local region is already informed by the quadratic fitting, we use large perturbation steps to explore far areas. We demonstrate the efficiency of our method on MNIST, CIFAR-10 and ImageNet datasets for various standard and robust models, as well as on Google Cloud Vision. The experimental results show that exploiting the loss landscape can help significantly reduce the number of queries and increase the success rate. Our codes are available at https://github.com/HoangATran/BABIES.

Tran, Hoang↗
DOE OSTI2025

TCC in the interior of moduli space and its implications for the string landscape and cosmology

We consider the classical Friedmann-Robertson-Walker solutions that describe a universe undergoing a transition from an accelerating expansion phase in the past to an eternal decelerating expansion phase in the future, driven by a scalar field evolving in a potential energy landscape. We show that any solution for which the accelerating phase violates the Trans-Planckian Censorship Conjecture (TCC), even in the interior of moduli space, never approaches the asymptotic vacuum with zero particles. Based on the assumption that the effective field theory must be valid for the vacuum on the asymptotic boundary, as motivated by holography and string theory, we argue that (multi-field) scalar potentials with such solutions are disallowed, thus strengthening the case for TCC. In particular, assuming the regularity of the future vacuum state in the string landscape, we derive results that imply a new set of highly-nonlinear constraints across the string landscape which in the absence of certain meta-stable vacua make realizing inflation impossible.

Cosmological models↗
DOE OSTI2022

Fine-tuned vs. natural supersymmetry: what does the string landscape predict?

A vast array of (metastable) vacuum solutions arise from string compactifications, each leading to different 4-d laws of physics. The space of these solutions, known as the string landscape, allows for an environmental solution to the cosmological constant problem. We examine the possibility of an environmental solution to the gauge hierarchy problem. We argue that the landscape favors softly broken supersymmetric models over particle physics models containing quadratic divergences, such as the Standard Model. We present a scheme for computing relative probabilities for supersymmetric models to emerge from the landscape. The probabilities are related to the likelihood that the derived value of the weak scale lies within the Agrawal et al. (ABDS) allowed window of values leading to atoms as we know them. This then favors natural SUSY models over unnatural (SUSY and other) models via a computable probability measure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Mode connectivity in the loss landscape of parameterized quantum circuits

Variational training of parameterized quantum circuits (PQCs) underpins many workflows employed on near-term noisy intermediate scale quantum (NISQ) devices. It is a hybrid quantum-classical approach that minimizes an associated cost function in order to train a parameterized ansatz. In this work we adapt the qualitative loss landscape characterization for neural networks introduced in Goodfellow et al. (2014); Li et al. (2017) and tests for connectivity used in Draxler et al. (2018) to study the loss landscape features in PQC training. We present results for PQCs trained on a simple regression task, using the bilayer circuit ansatz, which consists of alternating layers of parameterized rotation gates and entangling gates. Multiple circuits are trained with 3 different batch gradient optimizers: stochastic gradient descent, the quantum natural gradient, and Adam. We identify large features in the landscape that can lead to faster convergence in training workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Spectral distortions to momentum and scalar exchanges by non-turbulent motion and patchy landscape variability

Modifications to the spectra of turbulent velocity and scalars and co-spectra of vertical fluxes of momentum and scalars due to patchy landscape heterogeneity and non-stationarity are explored for a Mediterranean ecosystem. About 9 months of high frequency measurements of the three velocity components, water vapor concentration, carbon dioxide concentration, and air temperature were analyzed for different seasons (spring/summer) and prevalent wind directions (southeast/northwest). The two wind directions sampled a contrast of clumped and patchy landscape comprised of olive trees (southeast) and wall bounded flow disturbed by the presence of few upwind trees (northwest). The measured spectra and co-spectra were also compared to theoretical scaling forms from stationary, planar homogeneous flow, in the absence of subsidence as derived from the Kansas experiment. To assess the role of low frequency non-turbulent motion on the spectral and co-spectral content, a 5-min Fourier cutoff was introduced and the analysis was limited to near-neutral conditions where the boundary layer depth is shallow compared to its unstable counterpart. It was shown that the velocity statistics were not appreciably impacted by the low-frequency motion causing non-stationarity. Moreover, the turbulent scalar fluxes were also shown not to be significantly impacted by such low frequency motion. The scalar variances were impacted, especially the water vapor variance and its concomitant spectral shape. When the non-turbulent motion was filtered, the scalar spectra at low wavenumbers followed expectations from the so-called attached eddy hypothesis (i.e. exhibited a $k^{-1}_x$ scaling with $k_x$ defining the longitudinal wavenumber) applicable for near-neutral conditions. For momentum co-spectra, the canonical shapes from the Kansas experiment appear to describe well the measurements here and in both dominant directions and seasons with some adjustment to the integral time scales based on wind direction. For the scalar co-spectra, deviations from the Kansas experiment were prevalent. The most noticeable and surprising deviations were their slow decay with increased sampling frequency at inertial subrange scales. This slow decay was shown not to contribute appreciably to the overall scalar fluxes. At those fine scales, predictions from local isotropy were expected to hold. The scalar co-spectral deviations from local isotropy were then discussed using a simplified co-spectral budget model where scalar–scalar co-spectra naturally emerged and the interplay between landscape heterogeneity and a scale-dependent pressure-scalar de-correlation time was postulated. It is also envisaged that the findings here offer a preliminary template for analyzing eddy-covariance data in situations that deviate from ideal conditions, especially regarding low-frequency modulations of scalar spectra and vertical scalar flux co-spectra.

Canopy turbulence↗
DOE OSTI2026

Organo-mineral interactions in active layer and permafrost soils along aging Arctic landscapes

Rising temperatures are accelerating permafrost thaw, exposing large soil organic carbon (SOC) stocks to microbial decomposition with implications for global climate. Understanding how permafrost carbon is stored and protected through associations with minerals is critical for predicting its vulnerability to decomposition upon thaw. However, how landscape age, substrate chemistry, and soil depth influence mineral associations remain relatively unexplored. We investigated organo-mineral associations in active layer and permafrost soils across a landscape age and geochemical gradient on Alaska’s North Slope, spanning three glaciated (~11,500–125,000 years) and one unglaciated site. Using selective dissolution extractions, X-ray diffraction, and Mössbauer spectroscopy, we characterized minerals and their relationship with SOC. The three recently deglaciated sites had low soil pH that decreased with age and greater abundances of pyrophosphate- and oxalate-extractable Al and Fe, whereas the oldest unglaciated site exhibited near-neutral pH, greater pyrophosphate-extractable Ca, and distinct mineralogy. Across sites, SOC was positively associated with Al and Fe mineral phases, with stronger relationships in acidic soils. Pyrophosphate-extractable Ca also showed strong relationships with SOC at the acidic sites (up to ~10x greater), suggesting that Ca-mediated protection may operate beyond traditionally recognized high-pH soils. Permafrost soils showed depth-related changes in pH, SOC, and Fe mineralogy, suggesting chemically active, heterogeneous layers may shape mineral dynamics and associated carbon. Our results highlight how landscape age, parent material, and depth create distinct geochemical environments that govern mineral-organic associations. As thaw exposes soil to new conditions, these mineral-mediated protection mechanisms may be altered, potentially affecting the permafrost carbon-climate feedback.

Synthetic Biology↗
DOE OSTI2024

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and don’t always predict therapeutic response. Here we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measured the proteome and phosphoproteome of 210 patients and combined them with genomics and transcriptomic measurements to identify four proteogenomic subtypes that complemented existing genetic subtypes. We then built a predictor to classify samples into subtypes based on 147 molecular features and mapped them to a ‘landscape’. Each region of this landscape corresponded to specific drug response patterns. We then built a drug response prediction model to identify drugs that target distinct subtypes. We can ultimately use these models to predict drug treatment response and prioritize treatments. Finally, we extended our models and mapped a series of cell lines representing various stages of quizartinib resistance into our subtype landscape, predicting and experimentally validating a switch in sensitivity to venetoclax to panobinostat, two drugs with very different mechanisms than quizartinib. Our results show how multi-omics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Using Metadynamics to Reveal Extractant Conformational Free Energy Landscapes

Understanding the impact of extractant functionalization on metal-binding energetics in liquid-liquid extraction is essential to guide the development of better separation processes. Traditionally, computational extractant design uses electronic structure calculations on metal-ligand clusters to determine the metal-binding energy of the lowest energy state. Although highly accurate, this approach does not account for all of the relevant physics encountered under experimental conditions. Such methodologies often neglect entropic contributions such as temperature effects and ligand flexibility, in addition to approximating solvent-extractant interactions with implicit solvent models. In this study, we use classical molecular dynamics simulations with an advanced sampling method, metadynamics, to map out extractant molecule conformational free energies in the condensed phase. Here we generate the complete conformational landscape in solution for a family of bidentate malonamide-based extractants with different functionalizations of the headgroup and the side chains. In particular, we show how such alkyl functionalization reshapes the free energy landscape, affecting the free energy penalty of organizing the extractant into the cis-like metal-binding conformation from the trans-like conformation of the free extractant in solution. Specifically, functionalizing alkyl tails to the center of the headgroup has a greater influence on increasing molecular rigidity and disfavoring the binding conformation than functionalizing side chains. These findings are consistent with trends in metal-binding energetics based on experimentally reported distribution ratios. We also consider a different bidentate extractant molecule, carbamoylmethylphosphine oxide, and show how the choice of solvent can further reshape the conformational energetic landscape. This study demonstrates the feasibility of using molecular dynamics simulations with advanced sampling techniques to investigate extractant conformational energetics in solution, which, more broadly, will enable extractant design that accounts for entropic effects and explicit solvation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Permafrost Landscape History Shapes Fluvial Chemistry, Ecosystem Carbon Balance, and Potential Trajectories of Future Change

Intensifying permafrost thaw alters carbon cycling by mobilizing large amounts of terrestrial substrate into aquatic ecosystems. Yet, few studies have measured aquatic carbon fluxes and constrained drivers of ecosystem carbon balance across heterogeneous Arctic landscapes. Here, we characterized hydrochemical and landscape controls on fluvial carbon cycling, quantified fluvial carbon fluxes, and estimated fluvial contributions to ecosystem carbon balance across 33 watersheds in four ecoregions in the continuous permafrost zone of the western Canadian Arctic: unglaciated uplands, ice-rich moraine, and organic-rich lowlands and till plains. Major ions, stable isotopes, and carbon speciation and fluxes revealed patterns in carbon cycling across ecoregions defined by terrain relief and accumulation of organics. In previously unglaciated mountainous watersheds, bicarbonate dominated carbon export (70% of total) due to chemical weathering of bedrock. In lowland watersheds, where soil organic carbon stores were largest, lateral transport of dissolved organic carbon (50%) and efflux of biotic CO 2 (25%) dominated. In watersheds affected by thaw-induced mass wasting, erosion of ice-rich tills enhanced chemical weathering and increased particulate carbon fluxes by two orders of magnitude. From an ecosystem carbon balance perspective, fluvial carbon export in watersheds not affected by thaw-induced wasting was, on average, equivalent to 6–16% of estimated net ecosystem exchange (NEE). Finally, in watersheds affected by thaw-induced wasting, fluvial carbon export approached 60% of NEE. Because future intensification of thermokarst activity will amplify fluvial carbon export, determining the fate of carbon across diverse northern landscapes is a priority for constraining trajectories of permafrost region ecosystem carbon balance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Constraining Erosion Rates and Landscape Evolution With In Situ 10 Be and 26 Al Cosmogenic Nuclides at Table Mountain, Antarctica

Abstract This study investigates surface weathering and sediment preservation at Table Mountain, a high‐elevation, hyperarid, polar landscape in the Transantarctic Mountains. We report cosmogenic nuclide concentrations ( 10 Be and 26 Al) in quartz from bedrock surfaces, erratic boulder lag, and cobbles embedded within Sirius Group sediments to quantify erosion rates. In situ 10 Be and 26 Al depth profiles from a 2.95 m permafrost core in the Sirius Group further constrain surface erosion rates and elucidate landscape stability. Measured 10 Be and 26 Al concentrations from two sandstone bedrock surfaces adjacent to Sirius Group sediments give erosion rates of 0.18–0.28 m/Myr. An erratic sandstone boulder within the lag above the Sirius Group yields erosion rates of ∼0.42 ± 0.03 m/Myr, whereas two cobbles embedded within the Sirius Group yield higher rates of 0.81–1.12 m/Myr. Depth profiles of in situ 10 Be and 26 Al indicate no vertical mixing of Sirius Group permafrost since deposition. Depth profile models are best explained by erosion rates of 0.53 +0.13 / −0.12 m/Myr, and an exposure age of 0.78 +0.06 / −0.08 Ma. We view the model “age” to represent the ∼0.8‐million‐year time‐scale for surface lowering equivalent to one attenuation length of cosmic ray production to achieve steady‐state conditions. Continual exhumation of embedded clasts from within the Sirius Group results in an accumulation of clasts forming the observed erosional lag deposit covering the landscape. Our erosion rates of the Sirius Group surface based on in situ 10 Be and 26 Al depth profiles are an order‐of‐magnitude larger than those based on meteoric 10 Be infiltration and further clarification is required.

58 GEOSCIENCES↗
DOE OSTI2023

Computational remodeling of an enzyme conformational landscape for altered substrate selectivity

Structural plasticity of enzymes dictates their function. Yet, our ability to rationally remodel enzyme conformational landscapes to tailor catalytic properties remains limited. Here, we report a computational procedure for tuning conformational landscapes that is based on multistate design of hinge-mediated domain motions. Using this method, we redesign the conformational landscape of a natural aminotransferase to preferentially stabilize a less populated but reactive conformation and thereby increase catalytic efficiency with a non-native substrate, resulting in altered substrate selectivity. Steady-state kinetics of designed variants reveals activity increases with the non-native substrate of approximately 100-fold and selectivity switches of up to 1900-fold. Structural analyses by room-temperature X-ray crystallography and multitemperature nuclear magnetic resonance spectroscopy confirm that conformational equilibria favor the target conformation. Our computational approach opens the door to targeted alterations of conformational states and equilibria, which should facilitate the design of biocatalysts with customized activity and selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗