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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Development of next generation tempered and ODS reduced activation ferritic/martensitic steels for fusion energy applications

Reduced activation ferritic/martensitic steels are currently the most technologically mature option for the structural material of proposed fusion energy reactors. Advanced next-generation higher performance steels offer the opportunity for improvements in fusion reactor operational lifetime and reliability, superior neutron radiation damage resistance, higher thermodynamic efficiency, and reduced construction costs. The two main strategies for developing improved steels for fusion energy applications are based on (1) an evolutionary pathway using computational thermodynamics modelling and modified thermomechanical treatments (TMT) to produce higher performance reduced activation ferritic/martensitic (RAFM) steels and (2) a higher risk, potentially higher payoff approach based on powder metallurgy techniques to produce very high strength oxide dispersion strengthened (ODS) steels capable of operation to very high temperatures and with potentially very high resistance to fusion neutron-induced property degradation. The current development status of these next-generation high performance steels is summarized, and research and development challenges for the successful development of these materials are outlined. In conclusion, material properties including temperature-dependent uniaxial yield strengths, tensile elongations, high-temperature thermal creep, Charpy impact ductile to brittle transient temperature (DBTT) and fracture toughness behaviour, and neutron irradiation-induced low-temperature hardening and embrittlement and intermediate-temperature volumetric void swelling (including effects associated with fusion-relevant helium and hydrogen generation) are described for research heats of the new steels.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗
DOE OSTI2020

Library for Evolutionary Algorithms in Python (LEAP)

There are generally three types of scientific software users: users that solve problems using existing science software tools, researchers that explore new approaches by extending existing code, and educators that teach students scientific concepts. Python is a general-purpose programming language that is accessible to beginners, such as students, but also as a language that has a rich scientific programming ecosystem that facilitates writing research software. Additionally, as high-performance computing (HPC) resources become more readily available, software support for parallel processing becomes more relevant to scientific software.There currently are no Python-based evolutionary computation frameworks that support all three types of scientific software users. Moreover, some support synchronous concurrent fitness evaluation that do not efficiently use HPC resources. We pose here a new Python-based EC framework that uses an established generalized unified approach to EA concepts to provide an easy to use toolkit for users wishing to use an EA to solve a problem, for researchers to implement novel approaches, and for providing a low-bar to entry to EA concepts for students. Additionally, this toolkit provides a scalable asynchronous fitness evaluation implementation friendly to HPC that has been vetted on hardware ranging from laptops to the world’s fastest supercomputer, Summit.

Coletti, Mark↗
DOE OSTI2018

Predicting phase behavior of grain boundaries with evolutionary search and machine learning

The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in the exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that can predict interface structure. Here we develop a computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search and we design a clustering analysis that automatically identifies different grain boundary phases. Its application to a model system of symmetric tilt boundaries in Cu uncovers an unexpected rich polymorphism in the grain boundary structures. We find new ground and metastable states by exploring structures with different atomic densities. Our results demonstrate that the grain boundaries within the entire misorientation range have multiple phases and exhibit structural transitions, suggesting that phase behavior of interfaces is likely a general phenomenon.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Implementation of ANL’s Mechanics Based Evolutionary Fatigue Modeling Through ABAQUS-WARP3D Based High-Performance Computing Framework

This report presents an update on the environmental fatigue research that is being conducted at Argonne National Laboratory in support of the Department of Energy’s Light Water Reactor Sustainability (LWRS) program. Argonne is developing a fully mechanistic fatigue evaluation approach without using empirical fatigue (S~N) curves. This approach is based on the fundamental concept of the time evolution of progressive fatigue damage rather than on the conventional S~N curve approaches using end-of-life data. In FY 2017, we performed extensive validation of this approach with respect to fatigue test data for 316 stainless steel [1]. This validation was performed for different loading cases, including constant, variable, and random amplitude. In the present FY 2018 semi-annual report, we present the further advances of Argonne’s environmental fatigue research work in the context for more practical applications. In this report, we discuss a methodology for fully mechanistic (i.e., not using S~N curves) fatigue life evaluation of reactor components subjected to realistic loading cycles, namely, design-basis loading cycles. The loading cycles include plant heat-up, full-power, and cool-down operations. As a test case, we considered a typical pressurized water reactor surge line, which is made of 316 SS. To perform the fatigue simulation for thousands of fatigue cycles in a computationally cost effective way, we modified our previous desktop-based finite element (FE) modeling approach to work in a high-performance computing (HPC) framework. For the HPC implementation, we developed a hybrid framework based on commercial FE software (ABAQUS), open-source FE software (WARP3D), and Argonne-developed evolutionary cyclic-plasticity modeling methods. We validated this HPC-based cycle-by-cycle damage model for the entire fatigue life of a Pressurized Water Reactor (PWR) surge line (SL) pipe with respect to assumed loading cycles. The simulated fatigue life was found to be 5855 cycles, which is 85% accurate as compared to the corresponding small-specimen-based experimental fatigue life (6914 cycles). Also, the simulated stress history captures the cyclic hardening and softening behavior of the material for entire fatigue cycles. The FE simulation of the PWR SL pipe was conducted in a reasonable time of 12.5 days. These results show the promise that a fully mechanistic (not using S~N curves) fatigue life evaluation of a safety-critical nuclear reactor component (or even other safety critical components like those in aircraft, aero-engines, etc.) is possible. We anticipate that this type of methodology will drastically reduce the uncertainly associated with conventional fatigue life estimates based on empirical S~N curves. We also proposed an FE model that is based on a hybrid full-component and single-element approach and that can readily be used by industry if HPC resources are not available. In this approach, a single-cycle FE simulation has to be performed first for the required loading cycle. Then, the resulting strain/stress profile at the hotspot (highest strain/stress location) is used as input strain/stress loading to the single-element FE model. The single-element FE model is run under multiple fatigue cycles as long as the failure criteria are achieved. The cycle number at failure gives the fatigue life of the component in question. This cost-effective framework does not require expensive HPC hardware and can be performed in a reasonable time by using a desktop computer. We demonstrated this approach with respect to PWR SL fatigue evaluation under two realistic design-basis loading cycles (simple and detailed). The associated results were validated with respect to test data obtained through fatigue testing of uniaxial specimens under the in-air condition at 300 °C. The approach is in the development stage and requires further improvement and validation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

DDT Polymorphism and the Lethality of Crystal Forms

Abstract DDT (1,1,1‐trichloro‐2,2‐bis(4‐chlorophenyl)ethane), a contact insecticide with a rich and controversial history since its activity was discovered in 1939, has long been thought to be monomorphic. Herein we report the discovery and characterization of a second polymorph, designated Form II, which can be isolated as single crystals, but converts very slowly at room temperature to the form reported previously, now designated as Form I. Computations based on an evolutionary algorithm for crystal structure prediction revealed that Forms I and II are among the four lowest energy crystal structures of fifty calculated. A preliminary study of the contact insecticidal activity toward fruit flies (Drosophila melanogaster) indicates that Form II is more active, suggesting opportunities for more effective solid‐state formulations that would allow reduced amounts of DDT, thereby minimizing environmental impact.

Yang, Jingxiang↗
DOE OSTI2017

DDT Polymorphism and the Lethality of Crystal Forms

DDT (1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethane), a contact insecticide with a rich and controversial history since its activity was discovered in 1939, has long been thought to be monomorphic. Herein we report the discovery and characterization of a second polymorph, designated Form II, which can be isolated as single crystals, but converts very slowly at room temperature to the form reported previously, now designated as Form I. Computations based on an evolutionary algorithm for crystal structure prediction revealed that Forms I and II are among the four lowest energy crystal structures of fifty calculated. In this work, a preliminary study of the contact insecticidal activity toward fruit flies (Drosophila melanogaster) indicates that Form II is more active, suggesting opportunities for more effective solid-state formulations that would allow reduced amounts of DDT, thereby minimizing environmental impact.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Protocol to detect dilution cycles in chemostat experiments and estimate growth rate slopes with linear modeling with R software chemostat_regression

Chemostat growth chambers measure optical density over time and require manual calculation of growth rates. Here, we present chemostat_regression, R software that enables users to automatically identify chemostat cycles and estimate growth rate using a linear regression approach. We describe steps for creating requisite software environment(s), formatting input data, executing the software via command line/RStudio/R-Shiny, interpreting results, assessing the validity of results, and modifying input parameters.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2020

Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems

Though robustness and resilience are commonly quoted as features of neuromorphic computing systems, the expected performance of neuromorphic systems in the face of hardware failures is not clear. In this work, we study the effect of failures on the performance of four different training algo-rithms for spiking neural networks on neuromorphic systems: two back-propagation-based training approaches (Whetstone and SLAYER), a liquid state machine or reservoir computing approach, and an evolutionary optimization-based approach (EONS). We show that these four different approaches have very different resilience characteristics with respect to simulated hardware failures. We then analyze an approach for training more resilient spiking neural networks using the evolutionary optimization approach. We show how this approach produces more resilient networks and discuss how it can be extended to other spiking neural network training approaches as well.

Schuman, Catherine↗
DOE OSTI2022

The Energetics of the Central Engine in the Powerful Quasar 3C 298

The compact steep-spectrum radio source 3C 298 (redshift of 1.44) has the largest 178 MHz luminosity in the Third Cambridge Revised Catalogue (3CR); its radio lobes are among the most luminous in the universe. The plasma state of the radio lobes is modeled with the aid of interferometric radio observations (in particular, the new Low Frequency Array observation and archival MERLIN data) and archival single-station data. It is estimated that the long-term time-averaged jet power required to fill these lobes with leptonic plasma is $\overline{Q}\,\approx 1.28\pm 0.51\times {10}^{47}\,\mathrm{erg}\,{{\rm{s}}}^{-1}$, rivaling the largest time-averaged jet powers from any quasar. Supporting this notion of extraordinary jet power is a 0.5–10 keV luminosity of ≈ 5.2 × 10 46 erg s –1 , comparable to luminous blazars, yet there is no other indication of strong relativistic beaming. We combine two new high signal-to-noise ratio optical spectroscopic observations from the Hobby-Eberly Telescope with archival Hubble Space Telescope, Two Micron All Sky Survey, and Galaxy Evolutionary Explorer data to compute a bolometric luminosity from the accretion flow of L bol ≈ 1.55 ± 0.15 × 10 47 erg s –1 . The ratio, $\overline{Q}/{L}_{\mathrm{bol}}\approx 1$, is the approximate upper limit for quasars. Characteristic of a large $\overline{Q}/{L}_{\mathrm{bol}}$, we find an extreme-ultraviolet (EUV) spectrum that is very steep (the "EUV deficit" of powerful radio quasars relative to radio-quiet quasars), and this weak ionizing continuum is likely a contributing factor to the relatively small equivalent widths of the broad emission lines in this quasar.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

EvoNet: A phylogenomic and systems biology approach to identify genes underlying plant survival in marginal, low‐N soils

The DOE‐BER “EvoNet” project investigates the genetic and molecular basis of plant resilience in extreme environments. We do this by identifying key genes that enable “extreme survivor” species to thrive in the nitrogen-poor soils of Chile’s hyper-arid Atacama Desert. Our collections focus on 32 Atacama extremophile species, including seven grass species with potential biofuel applications. To identify genes-of-importance to survival we compared genomic and transcriptomic profiles of extremophile species that thrive in the Atacama to those of closely related “sister” species from nitrogen-rich arid and mesic regions of California. Deep RNA sequencing and de novo transcriptome assembly across these triplet species sets supported a phylogenomic framework for identifying positively selected genes associated with adaptive divergence. Our integrative analysis combined ecological and environmental data, metagenomics, evolutionary and systems biology, and metabolomics. This enabled us to create an unprecedented framework for systematically understanding how non-model plants have adapted to survive in extreme conditions. Our resulting database of positively selected ortholog groups in the extremophile plants offers promising targets for engineering crop and biofuel species with enhanced resilience to drought and extreme weather. Additionally, our newest dataset explores and exploits a complementary metabolomic approach. This new aspect provides innovative strategies to manipulate plant cell metabolism, further supporting efforts to improve agricultural productivity in the face of extreme climates. Importantly, our combined evolutionary- and metabolomic-based strategies focused on convergent patterns of adaptation, providing a genetic and metabolomic toolkit for improving crop and biofuel resilience across diverse plant species. Finally, our novel exploration of ecological and evolutionary dynamics delivered to the community a phylogenomic computational pipeline called “PhyloGeneious.” Our continued adaptations of this pipeline are publicly available to expedite evolutionary genomic research for future scientific discoveries. In total, our DOE-BER has provided genomic, metabolomic, and computational strategies to understand how extremophile plants provide evolutionary and physiological targets for improving agricultural and biofuel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A GPU‐Accelerated Generative Adversarial Model for Causal Inference

We develop a GPU-accelerated machine learning generative adversarial model designed to facilitate causal inferences from observational data. Our model's theoretical framework is conceptualized in a manner that is amenable to being operable and scalable for high-performance computing platforms. We leverage GPU acceleration to develop a parallel evolutionary algorithm to achieve large-scale parallel computation of the model within a now widely accessible computing platform. This capability both enhances computational speedup and efficiency and also extends the use of the model to a broader range of substantive research domains while maintaining the underlying theoretical properties of the model.

GPU↗
DOE OSTI2019

Program Fuzzing on High Performance Computing Resources

American Fuzzy Lop (AFL) is an evolutionary fuzzer that is strategically implemented as a tool for discovering bugs in software during vulnerability research. This work seeks to understand how to best implement AFL on the High-Performance Computing resources available on the unclassified network at Sandia National Laboratories. We investigate various methods of executing AFL, requesting varying numbers of tasks on single compute nodes with 36 physical cores and 72 total threads. A Python script called Blue Claw is presented as an automated testbed generator tool to assist in the tedious process of creating and executing experiments of any scale and duration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A quantitative lens on anaerobic life: leveraging the state-of-the-art fluxomics approach to explore clostridial metabolism

Quantitative understanding of clostridial metabolism is of longstanding interest due to the importance of Clostridia as model anaerobes, biotechnology workhorses, and contributors to evolutionary history and ecosystem. Current computational methods such as flux balance analysis-based construction of clostridial metabolism in genome scale provide a fundamental framework for metabolic analysis. However, this method alone is inadequate to characterize cellular metabolic activity. Experiment-driven approaches including isotope tracer-based fluxomics in association with genetic and biochemical methods are needed to gain a more comprehensive understanding. Here we focus on typical examples where these integrated approaches have contributed to the identification of new metabolic pathways and quantification of metabolic fluxes in Clostridia. Here, we also highlight the opportunities and challenges of cutting-edge fluxomics approaches such as machine learning modeling, deuterium tracer approach, and high throughput flux phenotyping in exploring clostridial metabolism with respect to inorganic carbon utilization, redox cofactor interconversion, and other key metabolic features.

09 BIOMASS FUELS↗
DOE OSTI2021

Low Power Hardware-In-The-Loop Neuromorphic Training Accelerator

The training process for spiking neural networks can be very computationally intensive. Approaches such as evolutionary algorithms may require evaluating thousands or millions of candidate solutions. In this work, we propose using neuromorphic cores implemented on a Xilinx Zynq system on chip to accelerate and improve the energy efficiency of the evaluation step of an evolutionary training approach. We demonstrate this can significantly reduce the required energy to evolve a network with some cases showing greater than 10 times improvement as compared to a CPU-only system.

Mitchell, Parker↗
DOE OSTI2022

Variational quantum reinforcement learning via evolutionary optimization

Abstract Recent advances in classical reinforcement learning (RL) and quantum computation point to a promising direction for performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here, we present two frameworks for deep quantum RL tasks using gradient-free evolutionary optimization. First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using amplitude encoding. Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL in the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

97 MATHEMATICS AND COMPUTING↗