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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Geometry-aware framework for deep energy method: An application to structural mechanics with hyperelastic materials

Here, in this work, we introduce a novel physics-informed framework named the Geometry-Aware Deep Energy Method (GADEM) for solving structural mechanics problems on different geometries. As the weak form of the physical system equation (or the energy-based approach) has demonstrated clear advantages compared to the strong form for solving solid mechanics problems, GADEM employs the weak form and aims to infer the solution on multiple shapes of geometries. Integrating a geometry-aware framework into an energy-based method results in an effective physics-informed deep learning model in terms of accuracy and computational cost. Different ways to represent the geometric information and to encode the geometric latent vectors are investigated in this work. We introduce a loss function of GADEM which is minimized based on the potential energy of all considered geometries. An adaptive learning method is also employed for the sampling of collocation points to enhance the performance of GADEM. We present some applications of GADEM to solve solid mechanics problems, including a loading simulation of a toy tire involving contact mechanics and large deformation hyperelasticity. The numerical results of this work demonstrate the remarkable capability of GADEM to infer the solution on various and new shapes of geometries using only one trained model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

De novo design of small beta barrel proteins

Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy–based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Designing robust energy policy packages under deep uncertainty: A multi-metric decision support framework

The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.

Decision-support method↗
DOE OSTI2020

Multi-task deep reinforcement learning for intelligent multi-zone residential HVAC control

In this short communication, a data-driven deep reinforcement learning (deep RL) method is applied to minimize HVAC users’ energy consumption costs while maintaining users’ comfort. The applied deep RL method's efficiency is enhanced by conducting multi-task learning that can achieve an economic control strategy for a multi-zone residential HVAC system in both cooling and heating scenarios. The applied multi-task deep RL method is compared with a rule-based benchmark case and a single-task deep deterministic policy gradient algorithm to verify its effective and generalized application in optimizing HVAC operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Deep quantum circuit simulations of low-energy nuclear states

Numerical simulation is an important method for verifying the quantum circuits used to simulate low-energy nuclear states. However, real-world applications of quantum computing for nuclear theory often generate deep quantum circuits that place demanding memory and processing requirements on conventional simulation methods. Here, we present advances in high-performance numerical simulations of deep quantum circuits to efficiently verify the accuracy of low-energy nuclear physics applications. Our approach employs novel methods for accelerating the numerical simulation including management of simulated mid-circuit measurements to verify projection based state preparation circuits. In this study, we test these methods across a variety of high-performance computing systems and our results show that circuits up to 21 qubits and more than 115,000,000 gates can be efficiently simulated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Validation of Hadron Mass Correction Schemes in Deep Inelastic Scattering at Low Energy Transfer

One method of describing the quark-gluon of the structure proton relies on factorizing its cross-section into a process-dependent short distance element and an independent long distance element that quantifies the quark and gluon momentum distributions within the proton. This research focuses on validating the kinematic range at which factorization theorems still accurately describe the cross-section, in particular for events that simulate scattering on large momentum quarks, that are sensitive to the details of the color mechanism. In this regime, effects which otherwise scale away at large energies, become non-negligible at low energies. The goal of this research is to assess the viability of an extended version of ?collinear factorization? recently proposed by J. Guerrero and A. Accardi, which includes kinematic corrections at quark level, known as ?hadron mass corrections?, to account for the masses of the quarks and the target. To accomplish this, deep inelastic scattering events are simulated using a realistic model of deep inelastic scattering, in which one may analytically calculate both the structure functions and the quark distribution functions. These simulated experiments are then used to test the viability of the proposed factorization theorem by fitting the quark distributions and comparing these to the analytically calculable ones. It is found that the viability of hadron mass corrections in low energy scattering experiments, such as those at Jefferson Lab, extends beyond the kinematic range suggested by analytic calculations alone, provided that residual higher twist corrections are simultaneously fitted to the data.

Krause, Andrew↗
DOE OSTI2021

Submerged wave energy converter for deep water operations

A submergible wave energy converter and method for using the same are described. Such a wave energy converter may be used for deep water operations. In one embodiment, the wave energy converter apparatus comprises an absorber having a body with an upper surface and a bottom surface and at least one power take-off (PTO) unit coupled to the absorber and configured to displace movement of the absorber body relative to a reference, where the power take-off unit is operable to perform motion energy conversion based on displacement of the absorber body relative to the reference in response to wave excitation, and where the power take-off unit is operable to return the absorber body from a displaced position to a predefined equilibrium position and to provide a force acting on the absorber body for energy extraction.

Lehmann, Marcus↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Criticality analysis of nuclear binding energy neural networks

Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics. However, these ANNs are typically treated as ‘black boxes’, with their architecture (width, depth, and weight/bias initialization) and the training algorithm and parameters chosen empirically by optimizing learning based on limited exploration. We test a non-empirical approach to understanding and optimizing nuclear physics ANNs by adapting a criticality analysis based on renormalization group flows in terms of the hyperparameters for weight/bias initialization, training rates, and the ratio of depth to width. This treatment utilizes the statistical properties of neural network initialization to find a generating functional for network outputs at any layer, allowing for a path integral formulation of the ANN outputs as a Euclidean statistical field theory. We use a prototypical example to test the applicability of this approach: a simple ANN for nuclear binding energies. We find that with training using a stochastic gradient descent optimizer, the predicted criticality behavior is realized, and optimal performance is found with critical tuning. However, the use of an adaptive learning algorithm leads to somewhat superior results without concern for tuning and thus obscures the analysis. Nevertheless, the criticality analysis offers a way to look within the black box of ANNs, which is a first step towards potential improvements in network performance beyond using adaptive optimizers.

artificial neural network↗
DOE OSTI2021

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable to physicists, chemists and biologists when designing new materials, catalysts, biological probes and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of the time required by commonly used methods such as density functional theory (DFT). However, many of these deep learning architectures require energy minimized molecular geometries as input, which is also computationally expensive, and decreasing the reproducibility and throughput of these methods. In this study, we demonstrate that accurate quantum chemical computations can be performed without optimized geometries by operating in the coordinate-free domain using deep learning on graph encodings. Furthermore, we also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The Wave architecture outperforms graph convolution architectures, particularly on complex molecules. Furthermore, the structures of these graph encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: What types of quantum chemical properties can be represented by local-variable models? We find that Wave, a local-variable model, is more accurately calculates quantum chemical properties. Graph convolutional architectures require global variables, and are not as effective as as Wave. We anticipate that coordinate-free, deep-learning models of quantum chemistry will become valuable tools in chemistry and biology, enabling researchers to rapidly screen chemical databases or identify new molecules using automated, de-novo design algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Bipolar Membranes Electrodialysis for Water Purification and Resource Recovery

Reverse osmosis concentrate (ROC) from inland brackish water desalination and wastewater recycling presents moderate salinity, posing significant challenges for treatment and disposal. Existing treatment technologies predominantly rely on energy-intensive thermal methods, large-area evaporation strategies, or deep-well disposal, which often result in the loss of water value. Electrified membrane brine treatment offers a promising alternative by utilizing renewable energy sources, enabling complete water recycling. This work proposes the development of next-generation bipolar membrane electrodialysis (BMED) systems capable of desalinating brine while simultaneously recovering valuable resources. However, several critical challenges remain in the areas of membrane engineering for BMED. These include: 1) decreased current efficiency during desalination due to the low conductivity of feed chambers, 2) fouling and scaling from multivalent ions and contaminants in the feed, and 3) the retention and carryover of ROC contaminants, including PFAS compounds. Our research focuses on the development of novel anion exchange membranes (AEMs) and cation exchange membranes (CEMs) based on synthesized multiblock copolymers. We investigate the effects of block length, ion exchange capacity (IEC), and polymer backbone structure on cation and anion transport (e.g., Na+, Cl-, OH-, H+) for both our synthesized membranes and commercial membranes. Fundamental properties of these membranes, such as water uptake, IEC, and ionic conductivity, are also evaluated to optimize performance for BMED applications.

08 HYDROGEN↗
DOE OSTI2020

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of $\sqrt{s}=13\,\text {TeV} $ at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 $\,\text {fb}^{-1}$. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to $\hbox {b}\bar{\hbox {b}}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Deep learning–driven rare-event sampling reveals an Ala7/d-Ala8 conformational hinge governing amphiphilic switching in cyclosporine A

Passive membrane permeability of N-methylated macrocyclic peptides remains poorly understood because high-energy conformational intermediates are rarely sampled by conventional methods. Using the deep learning–driven rare-event sampling framework ES-CRUMPLE, we show that cyclosporine A accesses transient amphiphilic states via a localized conformational hinge at Ala7/d-Ala8, revealing a kinetic design handle that is invisible to population-based analyses.

Lindberg, Edward T [University of Tennessee, Knoxv↗
DOE OSTI2020

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics

We present a novel application of the machine learning / artificial intelligence method called boosted decision trees to estimate physical quantities on field programmable gate arrays (FPGA). The software package fwXmachina features a new architecture called parallel decision paths that allows for deep decision trees with arbitrary number of input variables. It also features a new optimization scheme to use different numbers of bits for each input variable, which produces optimal physics results and ultraefficient FPGA resource utilization. Problems in high energy physics of proton collisions at the Large Hadron Collider (LHC) are considered. Estimation of missing transverse momentum (E T miss ) at the first level trigger system at the High Luminosity LHC (HL-LHC) experiments, with a simplified detector modeled by Delphes, is used to benchmark and characterize the firmware performance. The firmware implementation with a maximum depth of up to 10 using eight input variables of 16-bit precision gives a latency value of $\mathcal{O}$(10) ns, independent of the clock speed, and $\mathcal{O}$(0.1)% of the available FPGA resources without using digital signal processors.

Instruments & Instrumentation↗
DOE OSTI2025

QCD Theory Meets Information Theory

We present a novel technique to incorporate precision calculations from quantum chromodynamics into fully differential particle-level Monte Carlo simulations. By minimizing an information-theoretic quantity subject to constraints, our reweighted Monte Carlo incorporates systematic uncertainties absent in individual Monte Carlo predictions, achieving consistency with the theory input in precision and its estimated systematic uncertainties. Our method can be applied to arbitrary observables known from precision calculations, including multiple observables simultaneously. It generates strictly positive weights, thus offering a clear path to statistically powerful and theoretically precise computations for current and future collider experiments. As a proof of concept, we apply our technique to event-shape observables at electron-positron colliders, leveraging existing precision calculations of thrust. Our analysis highlights the importance of logarithmic moments of event shapes, which have not been previously studied in the collider physics literature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗