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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational evolution of high-performing unfused non-fullerene acceptors for organic solar cells

Materials optimization for organic solar cells (OSCs) is a highly active field, with many approaches using empirical experimental synthesis, computational brute force to screen a subset of chemical space, or generative machine learning methods that often require significant training sets. While these methods may find high-performing materials, they can be inefficient and time-consuming. Genetic algorithms (GAs) are an alternative approach, allowing for the “virtual synthesis” of molecules and a prediction of their “fitness” for some property, with new candidates suggested based on good characteristics of previously generated molecules. In this work, a GA is used to discover high-performing unfused non-fullerene acceptors (NFAs) based on an empirical prediction of power conversion efficiency (PCE) and provides design rules for future work. The electron-withdrawing/donating strength, as well as the sequence and symmetry, of those units are examined. The utilization of a GA over a brute-force approach resulted in speedups up to 1.8 × 10 12 . New types of units, not frequently seen in OSCs, are suggested, and in total 5426 NFAs are discovered with the GA. Of these, 1087 NFAs are predicted to have a PCE greater than 18%, which is roughly the current record efficiency. While the symmetry of the sequence showed no correlation with PCE, analysis of the sequence arrangement revealed that higher performance can be achieved with a donor core and acceptor end groups. Future NFA designs should consider this strategy as an alternative to the current A-D-A'-D-A architecture.

14 SOLAR ENERGY↗
DOE OSTI2025

An experimentally informed design process for future inertial confinement fusion facilities

The achievement of ignition in the laboratory has renewed interest in defining the requirements for a future high-gain inertial confinement fusion (ICF) facility. Our best chance of predicting future ICF performance is with 3-D radiation hydrodynamic simulations that have been benchmarked against experimental data, but their high computational cost is prohibitive for use in practical design studies. We introduce a hierarchical approach where 3-D simulations are tuned to match experimental measurements and used to train 3-D degradation models in 1-D simulations allowing for accurate predictions over the entire OMEGA direct-drive database. A genetic algorithm was used in combination with the trained 1-D simulations to search for optimal direct-drive implosion designs at driver energies ranging from 20 kJ to 10 MJ. As the fidelity of 3-D codes improves, this approach will provide a viable experimentally informed tool for defining the next ICF facility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Numerical parameter optimizations of the Ignition and Growth model for a HMX plastic bonded explosive

We present here a novel method for optimizing parameters of the Ignition and Growth (I&G) reactive flow model for high explosives. The I&G model predicts the shock initiation response of explosives subjected to dynamic loading. However, calibrating the model is a time-consuming process because it has several parameters, typically requires multiple data sets, and there are no direct correlations between parameters and data. In this study, we couple a scalable optimization algorithm to simulations of shock initiation experiments in the multi-physics code ALE3D. We develop four I&G model parameter sets for the HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane)-based explosive LX-07 [90 wt. % HMX, 10 wt. % Viton A] based on minimizing the difference between calculations and measurements at the embedded pressure gauges in 1D gun shock experiments. Our study shows that a cost function based on both shock time of arrival and pressure pulse shape demonstrates the best agreement with experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Real-time tracking of protein unfolding with time-resolved x-ray solution scattering

The correct folding of proteins is of paramount importance for their function, and protein misfolding is believed to be the primary cause of a wide range of diseases. Protein folding has been investigated with time-averaged methods and time-resolved spectroscopy, but observing the structural dynamics of the unfolding process in real-time is challenging. Here, we demonstrate an approach to directly reveal the structural changes in the unfolding reaction. We use nano- to millisecond time-resolved x-ray solution scattering to probe the unfolding of apomyoglobin. The unfolding reaction was triggered using a temperature jump, which was induced by a nanosecond laser pulse. We demonstrate a new strategy to interpret time-resolved x-ray solution scattering data, which evaluates ensembles of structures obtained from molecular dynamics simulations. We find that apomyoglobin passes three states when unfolding, which we characterize as native, molten globule, and unfolded. The molten globule dominates the population under the conditions investigated herein, whereas native and unfolded structures primarily contribute before the laser jump and 30 μs after it, respectively. The molten globule retains much of the native structure but shows a dynamic pattern of inter-residue contacts. Our study demonstrates a new strategy to directly observe structural changes over the cause of the unfolding reaction, providing time- and spatially resolved atomic details of the folding mechanism of globular proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Drive-pressure optimization in ramp-wave compression experiments through differential evolution

Ramp-wave dynamic-compression experiments are used to examine quasi-isentropic loading paths in materials. The gradual and continuous increase in pressure created by ramp waves make these types of experiments ideal for studying nonequilibrium material behavior, such as solidification kinetics. In ramp-wave compression experiments, the input drive pressure to the experimental setup may be exerted through one of a number of different mechanisms (e.g., magnetic fields, gas-gun-driven impactors, or high-energy lasers) and is generally required for simulating such experiments. Yet, regardless of the specific mechanism, this drive pressure cannot be measured directly (measurements are generally taken at a location near the back of the experimental setup through a transparent window), leading to an inverse problem where one must determine the drive pressure at the front of the experimental setup (i.e., the input) that corresponds to the particle velocity (the output) measured near the back of the experimental setup. Furthermore, we solve this inverse problem using a heuristic optimization algorithm, known as differential evolution, coupled with a multiphysics, hydrodynamics code that simulates the compression of the experimental setup. By running many rounds of forward simulations of the experimental setup, our optimization process iteratively searches for a drive pressure that is optimized to closely reproduce the experimentally measured particle velocity near the back of the experimental setup. While our optimization methodology requires a significant number of hydrodynamics simulations to be conducted, many of these can be performed in parallel, which greatly reduces the time cost of our methodology. One novel aspect of our method for determining the drive pressure is that it does not require physical modeling of the drive mechanism and can thus be broadly applied to many types of ramp-compression experiments, regardless of the drive mechanism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Aperiodic optical coatings for neutral-color semi-transparent organic photovoltaics

Semi-transparent organic photovoltaics (ST-OPVs) have the potential for integration with windows for ubiquitous power generating applications. Typically, such applications require that ST-OPVs be neutrally transparent across the visible and exhibit both a high average photopic transmittance (APT) and color rendering index, as well as iso-energetic chromaticity coordinates. In this work, we demonstrate the design and use of optical coatings to achieve ST-OPVs with a neutral visible transmittance of APT = 50%, a power conversion efficiency of 8.3%, and optical properties that are independent of a ± 30° variation in the solar angle of incidence. As a result, these simple optical coatings are rapidly designed using a genetic algorithm and transfer matrix formalism.

14 SOLAR ENERGY↗
DOE OSTI2024

Machine learning assisted search for Fe–Co–C ternary compounds with high magnetic anisotropy

We employ a machine learning (ML)-guided framework to explore rare earth free magnetic materials, specifically focusing on Fe–Co–C ternary compounds for potential use in permanent magnets. Utilizing a specifically trained crystal graph convolutional neural network model, we efficiently screen a vast space of nearly a million substitutional structures to select 620 promising structures for further investigation by first-principles calculation. We predict five low-energy metastable Fe–Co–C compounds with formation energy less than 150 meV/atom above the convex hull. These compounds exhibit high magnetization (Js > 1.0 T) and significant magnetic anisotropy (K1 > 1.0 MJ/m3), making them promising candidates for permanent magnet applications. The phonon calculations indicate these compounds are dynamically stable. Our ML-guided framework demonstrates the utility of rapidly identifying novel materials with tailored magnetic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bridging material models across scales: An integrated approach to equation of state and molecular dynamics modeling of copper

New uncertainty-aware equation of state (EOS) and electrical conductivity (EC) models for copper have been developed. The multiphase EOS/EC models are fit to experimental solid/liquid EC isobar measurements as well as density-functional theory molecular dynamics (DFT-MD) EC calculations in both expanded and compressed regimes (0.1–16 g/ cm 3 ⁠). The liquid and solid EOS phases were fit to available experimental data along with additional DFT-MD data over the same range as the EC. Leveraging the DFT-MD data, a corresponding machine-learned interatomic potential (MLIAP) for copper was trained using genetic-algorithm optimization. The copper MLIAP was constrained by EOS shock points at high compressions. The final EOS bounded MLIAP proves to be stable over a large density range (approximately 0.1–20 g/ cm 3 ) with good agreement to an isothermal compression curve, shock Hugoniot, and liquid speed of sound measurements at high pressures (100s of GPa).

Acoustic measurements and instrumentation↗
DOE OSTI2022

Enhanced resonant ultrasound spectroscopy for measurement of the elastic properties of multi-material systems

Understanding the elastic properties of materials is critical for their safe incorporation and predictable performance. Current methods of bulk elastic characterization often have notable limitations for in situ structural applications, with usage restricted to simple geometries and material distributions. To address these existing issues, this study sought to expand the capabilities of resonant ultrasound spectroscopy (RUS), an established nondestructive evaluation method, to include the characterization of isotropic multi-material samples. In this work, finite-element-based RUS analysis consisted of numerical simulations and experimental testing of composite samples comprised of material pairs with varying elasticity and density contrasts. Utilizing genetic algorithm inversion and mode matching, our results demonstrate that elastic properties of multi-material samples can be reliably identified within several percent of known or nominal values using a minimum number of identified resonance modes, given sample mass is held consistent. The accurate recovery of material properties for composite samples of varying material similarity and geometry expands the pool of viable samples for RUS and advances the method towards in situ inspection and evaluation.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A Comparison of Neuromorphic Classification Tasks

A variety of neural network models and machine learning techniques have arisen over the past decade, and their successes with image classification have been stunning. With other classification tasks, selecting and configuring a neural network solution is not straightforward. In this paper, we evaluate and compare a variety of neural network models, trained by a variety of machine learning techniques, on a variety of classification tasks. While Deep Learning typically exhibits the best classification accuracy, we note the promise of Reservoir Computing, and evolutionary optimization on spiking neural networks. In many cases, these technologies perform as well as, or better than Deep Learning, and the resulting networks are much smaller than their Deep Learning counterparts.

Reynolds, John↗
DOE OSTI2019

Island model for parallel evolutionary optimization of spiking neuromorphic computing

Parallel genetic algorithms (PGAs) can be used to accelerate optimization by exploiting large-scale computational resources. In this work, we describe a PGA framework for evolving spiking neural networks (SNNs) for neuromorphic hardware implementation. The PGA framework is based on an islands model with migration. We show that using this framework, better SNNs for neuromorphic systems can be evolved faster.

Schuman, Catherine↗
DOE OSTI2021

Understanding the Angular Momentum Evolution of T Tauri and Herbig Ae/Be Stars

We investigate a sample of six Herbig Ae/Be stars belonging to the Orion OB1 association, as well as 73 low-mass objects, members of the σ Orionis cluster, in order to explore the angular momentum evolution at early stages of evolution, and its possible connection with main-sequence Ap/Bp magnetic stars. Using FIES and HECTOCHELLE spectra, we obtain projected rotational velocities through two independent methods. Individual masses, radii, and ages are computed using evolutionary models, distance, and cluster extinction. Under the assumption that similar physical processes operate in both T Tauri and Herbig Ae/Be stars, we construct snapshots of the protostar’s rotation against mass during the first 10 Myr with the aid of a rotational model that includes a variable disk lifetime, changes in the stellar moment of inertia, a dipolar magnetic field with variable strength, and angular momentum loss through stellar winds powered by accretion. We use these snapshots, as well as the rotational data, to infer a plausible scenario for the angular momentum evolution. We find that magnetic field strengths of a few kilo-Gauss at 3 Myr are required to match the rotational velocities of both groups of stars. Models with masses between 2 M {sub ⊙} and 3 M {sub ⊙} display larger angular momentum values by a factor of ~3, in comparison to stars of similar spectral types on the main sequence. Even though some quantitative estimates on this dramatic decrease with age for Ap/Bp magnetic main-sequence stars are presented, the results obtained for the angular momentum evolution do not explain their low rotation rates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Origin and Evolution of Magnetic Field in PMS Stars: Influence of Rotation and Structural Changes

During stellar evolution, especially in the pre-main-sequence phase, stellar structure and rotation evolve significantly, causing major changes in the dynamics and global flows of the star. We wish to assess the consequences of these changes on stellar dynamo, internal magnetic field topology, and activity level. To do so, we have performed a series of 3D HD and MHD simulations with the ASH code. We choose five different models characterized by the radius of their radiative zone following an evolutionary track computed by a 1D stellar evolution code. These models characterized stellar evolution from 1 to 50 Myr. By introducing a seed magnetic field in the fully convective model and spreading its evolved state through all four remaining cases, we observe systematic variations in the dynamical properties and magnetic field amplitude and topology of the models. The five MHD simulations develop a strong dynamo field that can reach an equipartition state between the kinetic and magnetic energies and even superequipartition levels in the faster-rotating cases. We find that the magnetic field amplitude increases as it evolves toward the zero-age main sequence. Moreover, the magnetic field topology becomes more complex, with a decreasing axisymmetric component and a nonaxisymmetric one becoming predominant. The dipolar components decrease as the rotation rate and the size of the radiative core increase. The magnetic fields possess a mixed poloidal-toroidal topology with no obvious dominant component. Moreover, the relaxation of the vestige dynamo magnetic field within the radiative core is found to satisfy MHD stability criteria. Hence, it does not experience a global reconfiguration but slowly relaxes by retaining its mixed stable poloidal-toroidal topology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Intelligent Reservoir Generation for Liquid State Machines using Evolutionary Optimization

Neuromorphic Computing is a burgeoning field of research. Many groups are exploring hardware architectures and theoretical ideas about spiking recurrent neural networks. The overarching goal is to exploit the low power promise of these neuromorphic systems. However, it is difficult to train spiking recurrent neural networks (SRNNs) to perform tasks and make efficient use of neuromorphic hardware. Reservoir Computing is an attractive methodology because it requires no tuning of weights for the reservoir itself. Yet, to find optimal reservoirs, manual tuning of hyperparameters such as hidden neurons, synaptic density, and natural structure is still required. Because of this, researchers often have to generate and evaluate many networks, which can result in non-trivial amounts of computation. This paper employs the reservoir computing technique (specifically liquid state machines) and genetic algorithms in order to develop useful networks that can be deployed on neuromorphic hardware. We build on past work in reservoir computing and genetic algorithms to demonstrate the power of combining these two techniques and the advantage it can provide over manually tuning reservoirs for use on classification tasks. We discuss the complexities of determining whether or not to use the genetic algorithms approach for liquid state machine generation.

Reynolds, John↗
DOE OSTI2025

A compendium of human gene functions derived from evolutionary modelling

A comprehensive, computable representation of the functional repertoire of all macromolecules encoded within the human genome is a foundational resource for biology and biomedical research. The Gene Ontology Consortium has been working towards this goal by generating a structured body of information about gene functions, which now includes experimental findings reported in more than 175,000 publications for human genes and genes in experimentally tractable model organisms 1,2 . Here, we describe the results of a large, international effort to integrate all of these findings to create a representation of human gene functions that is as complete and accurate as possible. Specifically, we apply an expert-curated, explicit evolutionary modelling approach to all human protein-coding genes. This approach integrates available experimental information across families of related genes into models that reconstruct the gain and loss of functional characteristics over evolutionary time. The models and the resulting set of 68,667 integrated gene functions cover approximately 82% of human protein-coding genes. The functional repertoire reveals a marked preponderance of molecular regulatory functions, and the models provide insights into the evolutionary origins of human gene functions. We show that our set of descriptions of functions can improve the widely used genomic technique of Gene Ontology enrichment analysis. The experimental evidence for each functional characteristic is recorded, thereby enabling the scientific community to help review and improve the resource, which we have made publicly available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Neural architecture search via similarity adaptive guidance

Evolutionary neural network architecture search (ENAS) has attracted the attention of many experts due to its global optimization capabilities to automatically search for convolutional neural network architectures based on the target task. The current search space for ENAS is not to design a fully structured network, but to search for smaller cell architectures to reduce search costs. However, blind search strategies do not effectively utilize the potential experience of the population. In order to utilize the potential experience learned by the current population to guide the evolutionary search of the population, we propose a similarity guided neural network architecture search algorithm based on cell architecture, which utilizes the similarity between pairwise architectures in the population as empirical knowledge learned by the population. Our proposed algorithm provides a novel method for calculating architecture similarity, which calculates architecture similarity separately from the cell and macro-structure. Then we decouple the connections and operations in the cell and calculate connection and operation similarity separately. In addition, we propose adaptive similarity selection and binary tournament selection strategies to enhance the algorithm’s global and local search capabilities and effectively explore the search space. Finally, we design an improved single-point crossover operator to enhance the local search ability of the evolutionary operator. The experimental results show that SAGNAS is a competitive algorithm that achieves 97.44% and 81.60% in CIFAR10 and CIFAR100 with only 1.9 GPU-days spent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Grain boundary phases in bcc metals

We report a computational discovery of novel grain boundary structures and multiple grain boundary phases in elemental body-centered cubic (bcc) metals represented by tungsten, tantalum and molybdenum. While grain boundary structures created by the γ-surface method as a union of two perfect half crystals have been studied extensively, it is known that the method has limitations and does not always predict the correct ground states. Here, we use a newly developed computational tool, based on evolutionary algorithms, to perform a grand-canonical search of a high-angle symmetric tilt and twist boundaries in tungsten, and we find new ground states and multiple phases that cannot be described using the conventional structural unit model. We use molecular dynamics (MD) simulations to demonstrate that the new structures can coexist at finite temperature in a closed system, confirming these are examples of different grain boundary phases. The new ground state is confirmed by first-principles calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗