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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Measurements of low-mode asymmetries in the areal density of laser-direct-drive deuterium–tritium cryogenic implosions on OMEGA using neutron spectroscopy

Areal density is one of the key parameters that determines the confinement time in inertial confinement fusion experiments, and low-mode asymmetries in the compressed fuel are detrimental to the implosion performance. The energy spectra from the scattering of the primary deuterium–tritium (DT) neutrons off the compressed cold fuel assembly are used to investigate low-mode nonuniformities in direct-drive cryogenic DT implosions at the Omega Laser Facility. For spherically symmetric implosions, the shape of the energy spectrum is primarily determined by the elastic and inelastic scattering cross sections for both neutron-deuterium and neutron-tritium kinematic interactions. Two highly collimated lines of sight, which are positioned at nearly orthogonal locations around the OMEGA target chamber, record the neutron time-of-flight signal in the current mode. An evolutionary algorithm is being used to extract a model-independent energy spectrum of the scattered neutrons from the experimental neutron time-of-flight data and is used to infer the modal spatial variations (l = 1) in the areal density. Experimental observations of the low-mode variations of the cold-fuel assembly (ρL 0 + ρL 1 ) show good agreement with a recently developed model, indicating a departure from the spherical symmetry of the compressed DT fuel assembly. As a result, another key signature that has been observed in the presence of a low-mode variation is the broadening of the kinematic end-point due to the anisotropy of the dense fuel conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

High pressure-induced distortion in face-centered cubic phase of thallium

The complex and unusual high-pressure phase transition of III-A (i.e. Al, Ga, and In) metals have been investigated in the last several decades because of their interesting periodic table position between the elements having metallic and covalent bonding. Here, our present first principles-based electronic structure calculations and experimental investigation have revealed the unusual distortion in face-centered cubic (f.c.c.) phase of the heavy element thallium (Tl) induced by the high pressure. We have predicted body-centered tetragonal (b.c.t) phase at 83 GPa using an evolutionary algorithm coupled with ab initio calculations, and this prediction has been confirmed with a slightly distorted parameter (2–√ × a-c)/c lowered by 1% using an angle-dispersive X-ray diffraction technique. The density functional theory (DFT)-based calculations suggest that s–p mixing states and the valence-core overlapping of 6s and 5d states play the most important roles for the phase transitions along the pathway h.c.p→f.c.c.→b.c.t.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Genetic programming for the nuclear many-body problem: a guide

Genetic Programming (GP) is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use GP to develop surrogate models to mitigate the computational costs of modeling atomic nuclei with ever increasing complexity. The computational burden escalates when uncertainty quantification is pursued, or when observables must be globally computed for thousands of nuclei. By studying three models in which the mean field depends on the total particle density self-consistently, we show that by constructing reduced order models supported by GP one can speed up many-body computations by several orders of magnitude with a negligible loss in accuracy.

dimensionality reduction↗
DOE OSTI2025

Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators

Abstract Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraints on energy requirements of the beam. Using historical accelerator data, we develop a physics-based surrogate model which is differentiable and allows for back-propagation of gradients. The results are evaluated in the form of a Pareto-front with two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Predicting the ground-state structure of sodium boride

Binary borides has been a subject of extensive research. However, the exact compositions and crystal structures of sodium borides remained controversial. Furthermore, using the ab initio variable-composition evolutionary algorithm, a new stable Na 2 B 30 with I2 1 2 1 2 1 symmetry (I2 1 2 1 2 1 –Na 2 B 30 ) is found, which is –7.38 meV / atom lower in energy than the Imma–Na 2 B 30 structure reported by experimentalists. Interestingly, the Imma–Na 2 B 30 structure is predicted to be a topological nodal line semimetal, which may result in superior electronic transport. In contrast, I2 1 2 1 2 1 –Na 2 B 30 is an ultrahard semiconductor with an unprecedented open-framework structure, whose interstitial helical boron sublattice enhances its hardness and energetic stability.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Magnetic borophenes from an evolutionary search

A computational methodology based on ab initio evolutionary algorithms and spin-polarized density functional theory was developed to predict two-dimensional magnetic materials. Its application to a model system borophene reveals an unexpected rich magnetism and polymorphism. A metastable borophene with nonzero thickness is an antiferromagnetic semiconductor from first-principles calculations, and can be further tuned into a half-metal by finite electron doping. In this borophene, the buckling and coupling among three atomic layers are not only responsible for magnetism, but also result in an out-of-plane negative Poisson's ratio under uniaxial tension, making it the first elemental material possessing auxetic and magnetic properties simultaneously.

2-dimensional systems↗
DOE OSTI2019

Anharmonic and Anomalous Trends in the High-Pressure Phase Diagram of Silicon

A multifaceted first-principles approach utilizing density functional theory, evolutionary algorithms, and lattice dynamics was used to construct the phase diagram of silicon up to 4 TPa and 26,000 K. These calculations predicted that (1) an anomalous sequence of face-centered cubic (fcc) to body-centered cubic (bcc) to simple cubic (sc) crystalline phase transitions occur at pressures of 2.87 TPa and 3.89 TPa, respectively, along the cold curve; (2) the orthorhombic phases of Imma and Cmce-16 appear on the phase diagram only when the anharmonic contribution to the Gibbs free energy is taken into account; and (3) a drastic change in the slope of the principal Hugoniot is observed if the negative thermal expansion coefficient of the cubic diamond phase is taken into account through anharmonic effects.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Study for material analogs of FeSb 2 : Material design for thermoelectric materials

Using the ab initio evolutionary algorithm (implemented in uspex) and electronic structure calculations we investigate the properties of a new thermoelectric material FeSbAs, which is a material analog of the enigmatic thermoelectric FeSb 2 . We utilize the density functional theory and the Gutzwiller method to check the energetics. We find that FeSbAs can be made thermodynamically stable above ~ 30 GPa. We investigate the electronic structure and thermoelectric properties of FeSbAs based on the density functional theory and compare with those of FeSb 2 . Above 50 K, FeSbAs has higher Seebeck coefficients than FeSb 2 . Upon doping, the figure of merit becomes larger for FeSbAs than for FeSb 2 . Another material analog FeSbP, was also investigated, and found thermodynamically unstable even at very high pressure. Regarding FeSb 2 as a member of a family of compounds (FeSb 2 , FeSbAs, and FeSbP) we elucidate what are the chemical handles that control the gaps in this series. Here, we also investigate solubility (As or P for Sb in FeSb 2 ) we found As to be more soluble. Finally, we study a two-band model for thermoelectric properties and find that the temperature dependent chemical potential and the presence of the ionized impurities are important to explain the extremum in the Seebeck coefficient exhibited in experiments for FeSb 2 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Two-dimensional Dirac semimetal based on the alkaline earth metal CaP 3

Using an evolutionary algorithm in combination with first-principles density-functional theory calculations, we identify a two-dimensional (2D) CaP 3 monolayer as a new Dirac semimetal due to inversion and nonsymmorphic spatial symmetries of the structure. This new topological material, composed of light elements, exhibits high structural stability (higher than the phase known in the literature), which is confirmed by thermodynamic and kinetic stability analysis. Moreover, it satisfies the electron filling criteria, so that its Dirac state is located near the Fermi level. The existence of the Dirac state predicted by the theoretical symmetry analysis is also confirmed by first-principles electronic band structure calculations. We find that the energy position of the Dirac state can be tuned by strain, while the Dirac state is unstable against an external electric field since it breaks the spatial inversion symmetry. In conclusion, our findings should be instrumental in the development of 2D Dirac fermions based on light elements for their application in nanoelectronic devices and topological electronics.

2-dimensional systems↗
DOE OSTI2024

A science-driven approach to optimize the design for a biological small-angle neutron scattering instrument

Biological small-angle neutron scattering (SANS) instruments facilitate critical analysis of the structure and dynamics of complex biological systems. However, with the growth of experimental demands and the advances in optical systems design, a new neutron optical concept is necessary to overcome the limitations of current instruments. This work presents an approach to include experimental objectives ( i.e. the science to be supported by a specific neutron scattering instrument) in the optimization of the neutron optical concept. The approach for a proposed SANS instrument at the Second Target Station of the Spallation Neutron Source at Oak Ridge National Laboratory, USA, is presented here. Further, the instrument is simulated with the McStas software package. The optimization process is driven by an evolutionary algorithm using McStas output data, which are processed to calculate an objective function designed to quantify the expected performance of the simulated neutron optical configuration for the intended purpose. Each McStas simulation covers the complete instrument, from source to detector, including realistic sample scattering functions. This approach effectively navigates a high-dimensional parameter space that is otherwise intractable; it allows the design of next-generation SANS instruments to address specific scientific cases and has the potential to increase instrument performance compared with traditional design approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Confocal depth-resolved fluorescence micro-X-ray absorption spectroscopy for the study of cultural heritage materials: a new mobile endstation at the Beijing Synchrotron Radiation Facility

A confocal fluorescence endstation for depth-resolved micro-X-ray absorption spectroscopy is described. A polycapillary half-lens defines the incident beam path and a second polycapillary half-lens at 90° defines the probe sample volume. An automatic alignment program based on an evolutionary algorithm is employed to make the alignment procedure efficient. This depth-resolved system was examined on a general X-ray absorption spectroscopy (XAS) beamline at the Beijing Synchrotron Radiation Facility. Sacrificial red glaze (AD 1368–1644) china was studied to show the capability of the instrument. As a mobile endstation to be applied on multiple beamlines, the confocal system can improve the function and flexibility of general XAS beamlines, and extend their capabilities to a wider user community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Evolving Larger Convolutional Layer Kernel Sizes for a Settlement Detection Deep-Learner on Summit

Deep-learner hyper-parameters, such as kernel sizes, batch sizes, and learning rates, can significantly influence the quality of trained models. The state of the art for finding optimal hyper-parameters generally uses a brute force, grid search approach, random search, or Bayesian-based optimization among other techniques. We applied an evolutionary algorithm to optimize kernel sizes for a convolutional neural network used to detect settlements in satellite imagery. Usually convolutional layer kernel sizes are small - typically one, three, or five - but we found that the system converged at, or near, kernel sizes of nine for the last convolutional layer, and that this occurred for multiple runs using two different datasets. Moreover, the larger kernel sizes had fewer false positives than the 3x3 kernel sizes found as optimal via a brute force uniform grid search. This suggests that this large kernel size may be leveraging patterns found in larger areal features in the source imagery, and that this may be generalized as possible guidance for similar remote sensing deep-learning tasks.

Coletti, Mark↗
DOE OSTI2026

Predicting Band-Gap of Inorganic Materials Using Neuromorphic Graph Learning

Predicting properties of inorganic materials is a heavily researched topic, with several new prediction approaches emerging as competitors. One such competitor is graph neural networks, which leverage the structure of the graph to aid in the prediction process. In this work, we propose integration of neuromorphic computation into the graph neural network pipeline. We call this approach Neuromorphic Graph Learning (NGL). We utilize the NGL approach to leverage evolutionary algorithms and a novel Spike Pipeline for Raster Analysis (SPIRE) for the prediction of band gap in inorganic materials.

Mulet, Ian [University of Tennessee (UT)]↗
DOE OSTI2017

Evolving bipartite authentication graph partitions

As large scale enterprise computer networks become more ubiquitous, finding the appropriate balance between user convenience and user access control is an increasingly challenging proposition. Suboptimal partitioning of users’ access and available services contributes to the vulnerability of enterprise networks. Previous edge-cut partitioning methods unduly restrict users’ access to network resources. This paper introduces a novel method of network partitioning superior to the current state-of-the-art which minimizes user impact by providing alternate avenues for access that reduce vulnerability. Networks are modeled as bipartite authentication access graphs and a multi-objective evolutionary algorithm is used to simultaneously minimize the size of large connected components while minimizing overall restrictions on network users. Lastly, results are presented on a real world data set that demonstrate the effectiveness of the introduced method compared to previous naive methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Identifying Vulnerabilities and Hardening Attack Graphs for Networked Systems

We investigate efficient security control methods for protecting against vulnerabilities in networked systems. A large number of interdependent vulnerabilities typically exist in the computing nodes of a cyber-system; as vulnerabilities get exploited, starting from low level ones, they open up the doors to more critical vulnerabilities. These cannot be understood just by a topological analysis of the network, and we use the attack graph abstraction of Dewri et al. to study these problems. In contrast to earlier approaches based on heuristics and evolutionary algorithms, we study rigorous methods for quantifying the inherent vulnerability and hardening cost for the system. We develop algorithms with provable approximation guarantees, and evaluate them for real and synthetic attack graphs.

attack graphs↗
DOE OSTI2019

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks

Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are designed and developed with the goal of having small, low power chips that can perform control and machine learning tasks. However, the power consumption of the developed hardware can greatly depend on the size of the network that is being evaluated on the chip. Furthermore, the accuracy of a trained SNN that is evaluated on chip can change due to voltage and current variations in the hardware that perturb the learned weights of the network. While efforts are made on the hardware side to minimize those perturbations, a software based strategy to make the deployed networks more resilient can help further alleviate that issue. In this work, we study Spiking Neural Networks in two neuromorphic architecture implementations with the goal of decreasing their size, while at the same time increasing their resiliency to hardware faults. We leverage an evolutionary algorithm to train the SNNs and propose a multiobjective fitness function to optimize the size and resiliency of the SNN. We demonstrate that this strategy leads to well-performing, small-sized networks that are more resilient to hardware faults.

Dimovska, Mihaela↗
DOE OSTI2014

WE-E-17A-07: Patient-Specific Mathematical Neuro-Oncology: Biologically-Informed Radiation Therapy and Imaging Physics

Purpose: To demonstrate a method of generating patient-specific, biologically-guided radiation therapy (RT) plans and to quantify and predict response to RT in glioblastoma. We investigate the biological correlates and imaging physics driving T2-MRI based response to radiation therapy using an MRI simulator. Methods: We have integrated a patient-specific biomathematical model of glioblastoma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated RT optimization to construct individualized, biologically-guided plans. Patient-individualized simulations of the standard-of-care and optimized plans are compared in terms of several biological metrics quantified on MRI. An extension of the PI model is used to investigate the role of angiogenesis and its correlates in glioma response to therapy with the Proliferation-Invasion-Hypoxia- Necrosis-Angiogenesis model (PIHNA). The PIHNA model is used with a brain tissue phantom to predict tumor-induced vasogenic edema, tumor and tissue density that is used in a multi-compartmental MRI signal equation for generation of simulated T2- weighted MRIs. Results: Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized RT plans would have a significant impact on delaying tumor progression, with Days Gained increases from 21% to 105%. For the T2- MRI simulations, initial validation tests compared average simulated T2 values for white matter, tumor, and peripheral edema to values cited in the literature. Simulated results closely match the characteristic T2 value for each tissue. Conclusion: Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for RT generated biologically-guided doses that decreased normal tissue dose and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma. Simulated T2-MRI is shown to be consistent with known physics of MRI and can be used to further investigate biological drivers of imaging-based response to RT.

60 APPLIED LIFE SCIENCES↗