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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning prediction of enzyme optimum pH

The relationship between pH and enzyme catalytic activity, especially the optimal pH (pH opt ) at which enzymes function, is critical for biotechnological applications. Hence, computational methods to predict pH opt will enhance enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at specific pH levels, and by elucidating sequence-function relationships. Here, in this study, we proposed and evaluated various machine learning methods for predicting pH opt , conducting extensive hyperparameter optimization and training over 11,000 model instances. Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pH opt , including proximity of residues to the catalytic centre and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pH opt prediction and will potentially speed up the development of enzyme technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Tunable noninteracting free-energy density functionals for high-energy-density physics applications

In this work, we introduce the concept of a tunable noninteracting free-energy density functional and present two examples realized: (i) via a simple one-parameter convex combination of two existing functionals and (ii) via the construction of a generalized gradient approximation (GGA) enhancement factor that contains one free parameter and is designed to satisfy a set of incorporated constraints. Functional (i), constructed as a combination of the local Thomas–Fermi and a pseudopotential-adapted GGA for the noninteracting free-energy, has already demonstrated its practical usability for establishing the high temperature end of the equation of state of deuterium [Phys. Rev. B 104, 144104 (2021)] and CHON resin [Phys. Rev. E 106, 045207 (2022)] for inertial confinement fusion applications. Hugoniot calculations for liquid deuterium are given as another example of how the application of computationally efficient orbital-free density functional theory (OF-DFT) can be utilized with the employment of the developed functionals. Once the functionals have been tuned such that the OF-DFT Hugoniot calculation matches the Kohn–Sham solution at some low-temperature point, agreement with the reference Kohn–Sham results for the rest of the high temperature Hugoniot path is very good with relative errors for compression and pressure on the order of 2% or less.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Correcting implicit solvation at metal/water interfaces through the incorporation of competitive water adsorption

Conventional continuum solvation models are ubiquitous in computational catalysis, including for describing metal/water interfaces, which are relevant to both solution-phase heterogeneous catalysis and electrocatalysis. Nonetheless, we find that such continuum models qualitatively fail to describe both the adsorption free energy and conformational preference for many organic molecules at such interfaces, largely due to the failure of continuum models to incorporate the role of competitive water adsorption. We develop a simple phenomenological model that accounts for competitive water adsorption and show that the model, when used in conjunction with continuum solvation, provides a dramatic improvement in the description of both adsorption and conformational preference. The model is also extended to additionally incorporate the influence of applied potential at the electrode surface, thus facilitating computationally efficient applications to scenarios including electrocatalysis.

Chemistry↗
DOE OSTI2016

Statistical mentoring at early training and career stages

At Los Alamos National Laboratory (LANL), statistical scientists develop solutions for a variety of national security challenges through scientific excellence, typically as members of interdisciplinary teams. At LANL, mentoring is actively encouraged and practiced to develop statistical skills and positive career-building behaviors. Mentoring activities targeted at different career phases from student to junior staff are an important catalyst for both short and long term career development. This article discusses mentoring strategies for undergraduate and graduate students through internships as well as for postdoctoral research associates and junior staff. Topics addressed include project selection, progress, and outcome; intellectual and social activities that complement the student internship experience; key skills/knowledge not typically obtained in academic training; and the impact of such internships on students’ careers. Experiences and strategies from a number of successful mentorships are presented. Feedback from former mentees obtained via a questionnaire is incorporated. As a result, these responses address some of the benefits the respondents received from mentoring, helpful contributions and advice from their mentors, key skills learned, and how mentoring impacted their later careers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Topological defects and topological materials

We report that defects in materials that interpolate between two orientation states such as domain walls and those which relate to some kind of material discontinuity, e.g. dislocations and disclinations, comprise topological defects. Noncoplanar vector arrangements, e.g. vortices, skyrmions, and possibly monopole-like excitations, also belong to this family of defects. Presence of such defects affects many of the macroscopic properties of materials, for example electronic transport and strength. Materials in which topological aspects, e.g. edge or surface states, affect the electronic, transport and other properties can be described as topological materials. Topological insulators, topological superconductors and certain Dirac materials, e.g. graphene and Weyl semimetals, belong to this class of materials. Lastly, these materials possess unusual functionalities potentially useful in a variety of information processing and quantum computation based applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Path integral factorization and the gravitational effective action

Abstract We discuss the factorization and continuity properties of fields in the Euclidean gravitational path integral with higher dimension operators constructed from powers of the Riemann tensor. We construct the boundary terms corresponding to the microcanonical ensemble and show that the saddle point approximation to the path integral with a quasilocal energy constraint generally yields a saddle point with discontinuous temperature. This extends a previous result for the Euclidean Schwarzschild–de Sitter geometry in Einstein gravity and shows that it is robust against at least some types of quantum corrections from heavy fields. As an application, we compute the entropy of SdS in D = 4 using the BTZ method. Our result matches the entropy calculated using Wald’s formula.

Astronomy & Astrophysics↗
DOE OSTI2021

Observational measures of halo properties beyond mass

Different properties of dark matter haloes, including growth rate, concentration, interaction history, and spin, correlate with environment in unique, scale-dependent ways. While these halo properties are not directly observable, galaxies will inherit their host haloes’ correlations with environment. In this paper, we show how these characteristic environmental signatures allow using measurements of galaxy environment to constrain which dark matter halo properties are most tightly connected to observable galaxy properties. Here, we show that different halo properties beyond mass imprint distinct scale-dependent signatures in both the galaxy two-point correlation function and the distribution of distances to galaxies’ kth nearest neighbours, with features strong enough to be accessible even with low-resolution (e.g. grism) spectroscopy at higher redshifts. As an application, we compute observed two-point correlation functions for galaxies binned by half-mass radius at $z$ = 0 from the Sloan Digital Sky Survey, showing that classic galaxy size models (i.e. galaxy size being proportional to halo spin) as well as other recent proposals show significant tensions with observational data. We show that the agreement with observed clustering can be improved with a simple empirical model in which galaxy size correlates with halo growth.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Universal clustering of dark matter in phase space

We have recently introduced a novel statistical measure of dark matter clustering in phase space, the particle phase-space average density (P 2 SAD). In a two-paper series, we studied the structure of P 2 SAD in the Milky Way-size Aquarius haloes, constructed a physically motivated model to describe it, and illustrated its potential as a powerful tool to predict signals sensitive to the nanostructure of dark matter haloes. In this work, we report a remarkable universality of the clustering of dark matter in phase space as measured by P 2 SAD within the subhaloes of host haloes across different environments covering a range from dwarf-size to cluster-size haloes (10 10 –10 15 M ⊙ ). Simulations show that the universality of P 2 SAD holds for more than seven orders of magnitude, over a 2D phase space, covering over three orders of magnitude in distance/velocity, with a simple functional form that can be described by our model. Invoking the universality of P 2 SAD, we can accurately predict the non-linear power spectrum of dark matter at small scales all the way down to the decoupling mass limit of cold dark matter particles. As an application, we compute the subhalo boost to the annihilation of dark matter in a wide range of host halo masses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Selection Metric for Photovoltaic Materials Screening Based on Detailed-Balance Analysis

The success of recently discovered absorber materials for photovoltaic applications has been generating increasing interest in systematic materials screening over the last years. However, the key for a successful materials screening is a suitable selection metric that goes beyond the Shockley-Queisser theory that determines the thermodynamic efficiency limit of an absorber material solely by its band-gap energy. Here, we develop a selection metric to quantify the potential photovoltaic efficiency of a material. Our approach is compatible with detailed balance and applicable in computational and experimental materials screening. We use the complex refractive index to calculate radiative and nonradiative efficiency limits and the respective optimal thickness in the high mobility limit. We also compare our model to the widely applied selection metric by Yu and Zunger [Phys. Rev. Lett. 108, 068701 (2012)] with respect to their dependence on thickness, internal luminescence quantum efficiency, and refractive index. Finally, the model is applied to complex refractive indices calculated via electronic structure theory.

14 SOLAR ENERGY↗
DOE OSTI2015

Collective I/O Tuning Using Analytical and Machine-Learning Models

The ever larger demand of scientific applications for computation and data is currently driving a continuous increase in scale of parallel computers. The inherent complexity of scaling up a computing systems in terms of both hardware and software stack exposes an increasing number of factors impacting the performance and complicating the process of optimization. In particular, the optimization of parallel I/O has become increasingly challenging due to increasing storage hierarchy and well known performance variability of shared storage systems. This paper focuses on model-based autotuning of the two-phase collective I/O algorithm from a popular MPI distribution on the Blue Gene/Q architecture. We propose a novel hybrid model, constructed as a composition of analytical models for communication and storage operations and black-box models for the performance of the individual operations. We perform an in-depth study of the complexity involved in performance modeling including architecture, software stack and noise. In particular we address this challenges of modeling the performance of shared storage systems by building a benchmark that helps synthesizing factors such as topology, file caching, and noise. The experimental results show that the hybrid approach produces significantly better results than state-of-the-art machine learning approaches and shows a higher robustness to noise, at the cost of a higher modeling complexity

I/O performance modeling↗
DOE OSTI2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multi-threaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0x and 6.8x on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3x over the multi-thread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗
DOE OSTI2021

Numerical Simulations of Noisy Variational Quantum Eigensolver Ansatz Circuits

This is a case study of the variational quantum eigensolver (VQE) method using numerical simulations to test the influence of noise on the accuracy of the underlying circuit ansatz. We investigate a computational chemistry application of VQE to calculate the electronic ground state and its energy for Sodium Hydride (NaH), a prototypical two-electron problem. Using a one-parameter ansatz derived from unitary coupled cluster (UCC) theory, we simulate the effects of noise on the energy expectation value and variance with respect to the ansatz parameter. These numerical simulations provide insights into the accuracy of the prepared quantum state and the efficiency of the classical optimizer that iteratively refines the ansatz. We conduct a comparative study between analytical results derived for the UCC ansatz in the absence of noise and the noisy numerical simulation results obtained using an isotropic depolarizing noise model for each gate. We also compare the relative increase in noise on logically equivalent UCC ansatz circuits generated by randomized compiling. Notably, we observe that the intrinsic variance in the energy due to the simplicity of the ansatz itself compares with the noise induced by the bare circuit.

Gowrishankar, Meenambika↗
DOE OSTI2023

An Incremental Gradient Method for Optimization Problems With Variational Inequality Constraints

We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an application in computation of the best equilibrium in nonlinear complementarity problems arising in transportation networks. We develop an iteratively regularized incremental gradient method where at each iteration, agents communicate over a directed cycle graph to update their solution iterates using their local information about the objective and the mapping. The proposed method is single-timescale in the sense that it does not involve any excessive hard-to-project computation per iteration. We derive nonasymptotic agent-wise convergence rates for the suboptimality of the global objective function and infeasibility of the VI constraints measured by a suitably defined dual gap function. Finally, the proposed method appears to be the first fully iterative scheme equipped with iteration complexity that can address distributed optimization problems with VI constraints over cycle graphs.

convergence↗
DOE OSTI2019

How Excitation Conditions Alter the Afterglow Characteristics of CsI:Tl,Sm Microcolumnar Films

Combining high light output with the microcolumnar structure, the CsI:Tl films significantly reduce the need for painful tradeoff between thickness (hence, stopping power) and spatial resolution. But both films and monocrystals share two important deficiencies that hinder their use in many high-speed imaging and computed tomography applications: a persistent afterglow and hysteresis. These properties receive the primary attention of this investigation. In our earlier work, we reported that codoping with Sm 2+ brings about a significant reduction in the afterglow of CsI:Tl. Here, focusing on the microcolumnar films, we examine this afterglow-suppressive effect over a wide range of exposure times, from milliseconds to seconds, and at temperatures between 0 °C and 60 °C. Our measurements reveal well-defined dependences on both parameters. At room temperature (20 °C), defined as the normal operating condition, a 10-ms X-ray pulse leaves an afterglow from the standard (Sm-free) CsI:Tl of about 1% at 2 ms after the end of the pulse, changing little as a function of pulselength. Doping with Sm 2+ , however, reduces that afterglow by at least an order of magnitude, although somewhat less so after longer (100 and 1000 ms) pulses. When we change the temperature, however, we find markedly different behavior. Here, we find that a temperature increase of only 60°, without changing the pulselength, cuts the afterglow of standard CsI:Tl films by a factor of 20. However, films of codoped CsI:Tl,Sm, whose afterglow has already been suppressed by the presence of Sm 2+ , show much less change as a function of either temperature or pulse duration. It is the presence of Sm 2+ that makes the biggest difference. As for hysteresis, we again see an improvement, with the performance of the codoped CsI:Tl,Sm films bettering that of the standard (Sm-free) films. After an exposure of 300 mR, the change in light output falls to within 1% of the original after 1 min, whereas standard CsI:Tl films require 30 min to reach the same level.

42 ENGINEERING↗
DOE OSTI2023

TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones

Tropical cyclones (TCs) are among the most destructive weather systems. Realistically and efficiently detecting and tracking TCs are critical for assessing their impacts and risks. In particular, the eye is a signature feature of a mature TC. Therefore, knowing the eyes’ locations and movements is crucial for both operational weather forecasts and climate risk assessments. Recently, a multilevel robustness framework has been introduced to study the critical points of time-varying vector fields. The framework quantifies the robustness (i.e., structural stability) of critical points across varying neighborhoods. By relating the multilevel robustness with critical point tracking, the framework has demonstrated its potential in cyclone tracking. An advantage is that it identifies cyclonic features using only 2D wind vector fields, which is encouraging as most tracking algorithms require multiple dynamic and thermodynamic variables at different altitudes. A disadvantage is that the framework does not scale well computationally for datasets containing a large number of cyclones. Herein this paper introduces a topologically robust physics-informed tracking framework (TROPHY) for TC tracking. The main idea is to integrate physical knowledge of TC to drastically improve the computational efficiency of multilevel robustness framework for large-scale climate datasets. First, during preprocessing, we propose a physics-informed feature selection strategy to filter 90% of critical points that are short-lived and have low stability, thus preserving good candidates for TC tracking. Second, during in-processing, we impose constraints during the multilevel robustness computation to focus only on physics-informed neighborhoods of TCs. We apply TROPHY to 30 years of 2D wind fields from reanalysis data in ERA5 and generate a number of TC tracks. In comparison with the observed tracks, we demonstrate that TROPHY can capture TC characteristics (e.g., frequency, intensity, duration, latitudes with maximum intensity, and genesis) that are comparable to and sometimes even better than a well-validated TC tracking algorithm that requires multiple dynamic and thermodynamic scalar fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Location generalizability of image-based air quality models

This paper is to be submitted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Computer Vision for Earth Observation workshop. The full paper abstract is below: The ability to rapidly quantify atmospheric pollutants is important both for global emissions monitoring and for mitigating the adverse effects that follow a hazardous chemical release. In the aftermath of a chemical release, imagery is often the only available resource to assess local conditions. Recent work has demonstrated initial success in predicting particulate matter pollution from imagery; however, these results are tied to a specific site and do not generalize to new geographic locations. In this work, we seek to understand how easily deep learning models generalize to new locations in the context of image-based air quality assessments, targeting two distinct tasks: (1) broad measures of particulate matter pollution, and (2) the mass of a given chemical released in hazardous plumes. For the latter, we focus on sulfur dioxide, a toxic aerosol and a major component of particulate matter pollution caused by industrial fossil fuel consumption. To develop a model that operates in the widest possible range of environments, we test different training strategies, including the use of new geolocation foundation models. The best performing models achieve >80% accuracy when evaluating unseen imagery at previously seen sites, but we find significant drops in performance when evaluating imagery from unseen sites, at best 65%. Additionally, we present the public release of the National Parks Air Quality Index Dataset, a new medium-sized dataset that pairs imagery with sensor-based air quality measurements at 15 different national parks.

Byler, Eleanor B. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2022

Investigation of Microwave Transducers for Linearity Dependence and Applications in Quantum Networking

Quantum devices have the potential to revolutionize applications in computing, communications, and sensing; however, current state-of-art resources must operate at extremely low temperatures, making the routing of microwave control and readout signals challenging to scale. Interest in microwave photonic solutions to this problem has grown in recent years, in which control signals are delivered to the cold stage via optical fiber, where they are converted to electrical signals through photodetection. Overall link performance depends strongly on the characteristics of the photodiode, yet detailed measurements of many detector properties remain lacking at cold temperatures. In this work, we examine and compare the performance of a modified uni-traveling carrier photodiode (MUTC-PD) at both room (300 K) and liquid nitrogen (80 K) temperatures, focusing in particular on responsivity, bandwidth, and linearity. In line with previous work, we find a sharp reduction in responsivity at 1550 nm as temperature decreases, while RF bandwidth remains steady. Interestingly, our linearity tests reveal that the RF output saturates more quickly at 80 K, suggesting reduced linearity with lower temperature, the cause of which is still under investigation. Our results should help contribute to the understanding and future design of highly linear cryogenic quantum links.

Bolton, Summer↗
DOE OSTI2022

A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model

Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and other types of surrogate modeling techniques offer cheaper and simpler ways to describe the dynamics of these systems but are inexact and introduce additional approximation errors. In order to overcome the computational difficulties of the full complex models, on one hand, and the limitations of surrogate models, on the other, this work proposes a new accelerated time-stepping strategy that combines information from both. This approach is based on the multirate infinitesimal general-structure additive Runge--Kutta framework. The inexpensive surrogate model is integrated with a small time step to guide the solution trajectory, and the full model is treated with a large time step to occasionally correct for the surrogate model error and ensure convergence. Here, we provide a theoretical error analysis, and several numerical experiments, to show that this approach can be significantly more efficient than using only the full or only the surrogate model for the integration.

Surrogate models↗