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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2-loop scattering on superstring and supermembrane in flat space

We consider the S-matrix of transverse scalar excitations on an infinite D = 10 GS superstring and D = 11 supermembrane in flat target space. We compute the 4-particle scattering amplitude in the 2-loop approximation and demonstrate that, like in the Nambu string case, the D = 10 GS string S-matrix does not contain non-trivial 2d UV divergences (UV pole not accompanied by terms with logarithms of momenta is an artifact of dimensional regularization). This is consistent with underlying integrability of this model which is maintaining by adding appropriate local counterterms. In the supermembrane case there are no 1-loop divergences but we find a genuine 2-loop UV pole. This demonstrates non-finiteness of the world-volume S-matrix of the M2 brane theory.

AdS-CFT Correspondence↗
DOE OSTI2022

Universal lifting in the D1-D5 CFT

We consider D1-D5-P states in the untwisted sector of the D1-D5 orbifold CFT where one copy of the seed CFT has been excited with a left-moving superconformal primary. Despite being BPS at the orbifold point, such states can ‘lift’ as the theory is deformed away from this point in moduli space. We compute this lifting at second order in the deformation parameter for arbitrary left-moving dimension h of this class of states. This result displays an interesting universality since the lifting does not depend on the details of the superconformal primary; it depends only on the dimension. In the large-dimension limit the lift scales as $\sqrt{h}$; it is observed that such scaling appears to be a universal property of the lift of D1-D5-P states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗
DOE OSTI2025

A Deep State Space Model for Rainfall‐Runoff Simulations

The classical way of studying the rainfall‐runoff processes in the water cycle relies on conceptual or physically‐based hydrologic models. Deep learning (DL) has recently emerged as an alternative and blossomed in the hydrology community for rainfall‐runoff simulations. However, the decades‐old Long Short‐Term Memory (LSTM) network remains the benchmark for this task, outperforming newer architectures like Transformers. In this work, we propose a State Space Model (SSM), specifically the Frequency Tuned Diagonal State Space Sequence (S4D‐FT) model, for rainfall‐runoff simulations. The proposed S4D‐FT is benchmarked against the established LSTM and a physically‐based Sacramento Soil Moisture Accounting model under in‐sample and out‐of‐sample simulation setups across 531 watersheds in the contiguous United States (CONUS). Results show that S4D‐FT is able to outperform the LSTM model across diverse regions under both simulation setups, especially for regions that feature snowmelt‐driven or intermittent flow regimes. In contrast, S4D‐FT tends to underperform in flashier, high‐magnitude flow regimes, likely due to its global state‐space convolution computation that emphasizes slow, storage‐driven dynamics, which makes it less effective at picking up short bursts and noisy spikes in the data. In summary, our pioneering introduction of the S4D‐FT for rainfall‐runoff simulations challenges the dominance of LSTM in the hydrology community and expands the arsenal of DL tools available for hydrological modeling.

Wang, Yihan [Univ. of Oklahoma, Norman, OK (United↗
DOE OSTI2021

Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions

Abstract Despite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitness functions. While deep neural networks (DNNs) can capture high-order epistatic interactions among the mutational sites, they tend to overfit to the small number of labeled sequences available for training. Here, we developed Epistatic Net (EN), a method for spectral regularization of DNNs that exploits evidence that epistatic interactions in many fitness functions are sparse. We built a scalable extension of EN, usable for larger sequences, which enables spectral regularization using fast sparse recovery algorithms informed by coding theory. Results on several biological landscapes show that EN consistently improves the prediction accuracy of DNNs and enables them to outperform competing models which assume other priors. EN estimates the higher-order epistatic interactions of DNNs trained on massive sequence spaces-a computational problem that otherwise takes years to solve.

Aghazadeh, Amirali (ORCID:0000000302230873)↗
DOE OSTI2025

Optimal 3D chemical imaging with multimodal electron tomography

Accurate mapping of nanoscale chemistry in three dimensions (3D) has been a longstanding challenge. Modern electron microscopy provides chemical images by electron energy loss spectroscopy (EELS) and energy dispersive x-ray spectrometry (EDX) but requires high fluences that damage specimens. In 3D, the requirements are worse; electron tomography demands many high-fluence chemical maps for reconstruction, creating a tradeoff between resolution, accuracy, and sample survival. Fused multimodal electron tomography (MM-ET) alleviates this requirement by leveraging lower-fluence high-angle annular dark-field (HAADF) images alongside a few chemical maps to dramatically improve chemical resolution. Here, experimental and computational parameter space is systematically explored to determine when MM-ET performs best. Ideal imaging conditions balance sample survival with resolution and chemical specificity; we recommend a tilt range of at least ± 70°, acquiring 40 equally spaced HAADF projections (signal-to-noise > 10), and 7 EELS/EDX maps of each chemistry (signal-to-noise > 4).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Demonstration of reduced neoclassical energy transport in Wendelstein 7-X

Research on magnetic confinement of high-temperature plasmas has the ultimate goal of harnessing nuclear fusion for the production of electricity. Although the tokamak is the leading toroidal magnetic-confinement concept, it is not without shortcomings and the fusion community has therefore also pursued alternative concepts such as the stellarator. Unlike axisymmetric tokamaks, stellarators possess a three-dimensional (3D) magnetic field geometry. The availability of this additional dimension opens up an extensive configuration space for computational optimization of both the field geometry itself and the current-carrying coils that produce it. Such an optimization was undertaken in designing Wendelstein 7-X (W7-X), a large helical-axis advanced stellarator (HELIAS), which began operation in 2015 at Greifswald, Germany. A major drawback of 3D magnetic field geometry, however, is that it introduces a strong temperature dependence into the stellarator’s non-turbulent ‘neoclassical’ energy transport. Indeed, such energy losses will become prohibitive in high-temperature reactor plasmas unless a strong reduction of the geometrical factor associated with this transport can be achieved; such a reduction was therefore a principal goal of the design of W7-X. In spite of the modest heating power currently available, W7-X has already been able to achieve high-temperature plasma conditions during its 2017 and 2018 experimental campaigns, producing record values of the fusion triple product for such stellarator plasmas. The triple product of plasma density, ion temperature and energy confinement time is used in fusion research as a figure of merit, as it must attain a certain threshold value before net-energy-producing operation of a reactor becomes possible. Here we demonstrate that such record values provide evidence for reduced neoclassical energy transport in W7-X, as the plasma profiles that produced these results could not have been obtained in stellarators lacking a comparably high level of neoclassical optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Emergence of competing electronic states from non-integer nuclear charges

Understanding many-electron phenomena with competing near-degenerate electronic states is of fundamental importance to chemistry and condensed matter physics. One of the most significant challenges for exploring such many-electron phenomena is the necessity for large system sizes in order to realize competing states, far beyond those practical for first-principles methods. Here, we show how allowing non-integer nuclear charges expands the space of computationally tractable electron systems that host competing electronic states. The emergence of competing electronic states from non-integer nuclear charges is exemplified in the simple 2-electron H 2 molecule and used to examine the microscopic structure of doped quasi-1D cuprate chains, showing how non-integer nuclear charges can open a window for first-principles calculations of difficult many-electron phenomena.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Determining best practices for using genetic algorithms in molecular discovery

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. Here, we show that using a self-termination method with a minimum Spearman’s rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

First simultaneous observation of co- and counter-current fast-ion losses in the ASDEX Upgrade tokamak

In ITER and future fusion power plants, the source of the fusion born alpha particles is almost isotropic in pitch angle, thus having co- and counter-current populations. For trapped ions, the co-current side of the orbit corresponds to its outer leg, while the counter-current side corresponds to the inner leg. Understanding the mechanisms responsible for the fast-ion losses (FILs) is critical for future magnetically confined fusion power plants. To further study the interplay of fast ions with plasma instabilities, a double pinhole collimator has been developed for a Fast-Ion Loss Detector (FILD) in the ASDEX Upgrade tokamak (AUG). This new FILD opens the operational window to simultaneous measurements of the co- and counter-current ion velocity-space. In this paper, the first results for the AUG double collimator FILD detector are shown. The commissioning of this new probe is carried out in H-mode plasmas with an on-axis magnetic field $B_0 = -2.5$ T, and a plasma current $I_{\textrm{p}} = 0.7$ MA. Simultaneous co- and counter-current FILs have been measured. Both have shown a similar dependence on Ion Cyclotron Resonance Heating (ICRH) power, where the main difference is the intensity of the losses, with the co-losses being an order of magnitude larger. Toroidal Alfvén eigenmode-coherent ICRH-only losses have been identified for the co-current ions. Additionally, the presence of Edge Localized Modes during the discharge were shown to increment Neutral Beam Injection prompt losses, while partially mitigating ICRH-driven losses on both co- and counter- sides of the velocity-space. Finally, a very trapped and high gyroradius losses, with an unclear origin, have been measured in the co- and counter-current velocity-space. The computed ion trajectories show that these ions remain permanently near the vessel wall, suggesting that they are accelerated within the scrape-off layer.

ELM↗
DOE OSTI2020

Uncertainty quantification of an empirical shell-model interaction using principal component analysis

Recent investigations have emphasized the importance of uncertainty quantification (UQ) in nuclear theory. Here, we carry out UQ for configuration-interaction shell-model calculations in the 1$\textit{s}$–0$\textit{d}$ valence space, investigating the sensitivity of observables to perturbations in the 66 parameters (matrix elements) of a high-quality empirical interaction. The large parameter space makes computing the corresponding Hessian numerically costly, so we compare a cost-effective approximation, using the Feynman-Hellmann theorem, to the full Hessian and find it works well. Diagonalizing the Hessian yields the principal components of the interaction: linear combinations of parameters ordered by sensitivity. This approximately decoupled distribution of parameters facilitates theoretical uncertainty propagation onto structure observables: electromagnetic transitions, Gamow-Teller decays, and dark matter-nucleus scattering matrix elements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Comparing pion production in transport simulations of heavy-ion collisions at 270⁢𝐴 MeV under controlled conditions

Within the TMEP, we present a detailed study of the performance of different transport models in Sn+Sn collisions at 270A MeV, and put particular emphasis on the production of pions and Delta resonances, which have been used as probes of the nuclear symmetry energy. We prescribe a common and rather simple physics model, and follow in detail the results of 4 BUU models and 6 QMD models. The nucleonic evolution of the collision and the nucleonic observables in these codes do not completely converge, but the differences among the codes can be understood as being due to several reasons: the basic differences between BUU and QMD models in the representation of the phase-space distributions, computational differences in the mean-field evaluation, and differences in the adopted strategies for the Pauli blocking in the collision integrals. For pionic observables, we find that a higher maximum density leads to an enhanced pion yield and a reduced pi- pi+ yield ratio, while a more effective Pauli blocking generally leads to a slightly suppressed pion yield and an enhanced pi- pi+ yield ratio. We specifically investigate the effect of the Coulomb force, and find that it increases the total pi- pi+ yield ratio but reduces the ratio at high pion energies, although differences in its implementations do not have a dominating role in the differences among the codes. Taking into account only the results of codes that strictly follow the homework specifications, we find a convergence of the codes in the final charged pion yield ratio to a 1 sigma deviation of about 5%. Furthermore, the uncertainty is expected to be reduced to about 1.6% if the same or similar strategies and ingredients, i.e., an improved Pauli blocking and calculation of the non-linear term in the mean-field potential, are similarly used in all codes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Magnetothermopower of Nodal-Line Semimetals

The search for materials with large thermopower is of great practical interest. Dirac and Weyl semimetals have recently proven to exhibit superior thermoelectric properties, particularly when subjected to a quantizing magnetic field. Here, we consider whether a similar enhancement arises in nodal-line semimetals, for which the conduction and valence band meet at a line or ring in momentum space. We compute the Seebeck and Nernst coefficients for arbitrary temperature and magnetic field and we find a wealth of different scaling regimes. Most strikingly, when a sufficiently strong magnetic field is applied along the direction of a straight nodal line or in the plane of a nodal ring, the large degeneracy of states leads to a large linear-in- B thermopower that is temperature independent even at low temperatures. Our results suggest that nodal-line semimetals may offer significant opportunity for efficient low-temperature thermoelectrics. Published by the American Physical Society 2024

Chakraborty, Poulomi (ORCID:0000000213969473)↗
DOE OSTI2025

Characterization and Optimization of the Fitting of Quantum Correlation Functions

This case study presents a characterization and optimization of an application code for extracting parton distribution functions from high energy electron-proton scattering data. Profiling this application code reveals that the phase-space density computation accounts for 93% of the overall execution time for a single iteration on a single core. When executing multiple iterations in parallel on a multicore system, the application spends 78% of its overall execution time idling due to load imbalance. We address these issues by first transforming the application code from Python to C++ and then tackling the application load imbalance via a hybrid scheduling strategy that combines dynamic and static scheduling. These techniques result in a 62% reduction in CPU idle time and a 2.46x speedup in overall execution time per node. In addition, the typically enabled power-management mechanisms in supercomputers (e.g., AMD Turbo Core, Intel Turbo Boost, and RAPL) can significantly impact intra-node scalability when more than 50% of the CPU cores are used. This finding underscores the importance of understanding system interactions with power management, as they can adversely impact application performance, and highlights the necessity of intra-node scaling tests to identify performance degradation that inter-node scaling tests might otherwise overlook.

Chuang, Pi-Yueh [Virginia Tech,Dept. of Computer S↗
DOE OSTI2023

Adaptive language model training for molecular design

Abstract The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a useful framework to incrementally generate molecules by applying mutations to known chemical structures. Recently, masked language models have been applied to automate the mutation process by leveraging large compound libraries to learn commonly occurring chemical sequences (i.e., using tokenization) and predict rearrangements (i.e., using mask prediction). Here, we consider how language models can be adapted to improve molecule generation for different optimization tasks. We use two different generation strategies for comparison, fixed and adaptive. The fixed strategy uses a pre-trained model to generate mutations; the adaptive strategy trains the language model on each new generation of molecules selected for target properties during optimization. Our results show that the adaptive strategy allows the language model to more closely fit the distribution of molecules in the population. Therefore, for enhanced fitness optimization, we suggest the use of the fixed strategy during an initial phase followed by the use of the adaptive strategy. We demonstrate the impact of adaptive training by searching for molecules that optimize both heuristic metrics, drug-likeness and synthesizability, as well as predicted protein binding affinity from a surrogate model. Our results show that the adaptive strategy provides a significant improvement in fitness optimization compared to the fixed pre-trained model, empowering the application of language models to molecular design tasks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Parallel Multigrid in Time and Space for Extreme-Scale Computational Science

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations, which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of programmatic interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures.

97 MATHEMATICS AND COMPUTING↗