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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An Atomistic Study of Reactivity in Solid-State Electrolyte Interphase Formation for Li/Li7P3S11

Lithium metal batteries offer superior volumetric and gravimetric specific capacities compared to those based on traditional graphite anodes. Although advancements in solid-state electrolytes address safety concerns, challenges remain, particularly regarding interphase formation in lithium metal anodes. This work presents a computational framework based on high-throughput first-principles density functional theory and machine-learning interatomic potentials (MLIPs) including automated iterative, active learning to enable robust computational exploration of interphase formation between lithium metal anodes and an inorganic solid-state electrolyte. As a demonstration, we apply the framework to a Li/Li7P3S11 interface and find that it accurately identifies the experimentally observed, thermodynamically stable interphase products as well as their overall spatial arrangement within a heterogeneous, amorphous layered structure, with Li2S domains of nanocrystallinity. Our simulations show two stages, a fast and slow diffusion reaction regime, that corroborate the relative phase formation rate of Li x P, Li2S, and Li3P. Using the Onsager transport theory, we capture time-dependent ionic diffusion within the reacting interface, including cross-correlation effects. We found that cross-correlation effects between Li-P and P-S ionic motion significantly influence P-ion diffusion, making it highly sensitive to the local environment and potentially leading to "kinetic trapping" of Li-P phases. The passivation of the interface is shown as the ionic fluxes all approach zero, effectively halting interphase growth.

Diffusion↗
DOE OSTI2025

High entropy alloys as catalysts: A focused review

A brief literature review of recent experimental and computational efforts on the use of high entropy alloys (HEAs) as catalysts is presented, while also sharing some perspectives and future insights. To fully broach the vast compositional possibilities of HEA materials, integrating computational modeling with high-throughput experimental synthesis and validation is necessary to accelerate their design and development. Once identified, specific HEAs can be a class of materials for the next generation of catalysts when addressing global challenges related to energy independence, commodity chemical production, environmental remediation, abating emissions, and modernized domestic supply chain resilience.

42 - ENGINEERING↗
DOE OSTI2020

4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique

The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO 2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the 'unrolling' provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.

25 ENERGY STORAGE↗
DOE OSTI2021

Accelerated optimization of pure metal and ligand compositions for light-driven hydrogen production

Photocatalytic hydrogen production is a promising alternative to traditional hydrogen production. To implement photocatalytic hydrogen production the development of efficient, sustainable, and stable catalysts is necessary, and overcoming the current challenges surrounding high dimensional search spaces requires both computational and experimental efforts. Utilizing photo driven processes, stable colloidal metal catalysts can be formed in situ for efficient hydrogen production from water. When considering colloidal catalysts, stability is typically a concern solved through the addition of supports or ligands. In this work, poly(ethylene glycol) methyl ether thiol acts as a stabilizing ligand eliminating the need for catalyst supports while providing stable and active nanoparticle catalysts for more than 45 hours of reaction time and illumination. These systems utilize molecular photosensitizers, water reduction catalysts, stabilizing ligands, water, a sacrificial reductant, and organic solvents, posing new challenges pertaining to the optimization of multi-variable systems. Design of experiments (DOE) is applied to accelerate the understanding of variable interactions and is used as a tool to rapidly optimize the compositions of Au, Cu, Ni, and Fe containing systems. Through a collaboration leveraging computation and experimentation (both high throughput and characterizations), optimized performance peaks were obtained for each of these metals alongside distinct mapping of expected activity associated with photosensitizer, metal, and ligand concentration variations. With the highly digitized workflow, this study allowed for comparative generalizations to be made regarding photo driven hydrogen production for all four metals.

08 HYDROGEN↗
DOE OSTI2023

High-throughput identification of spin-photon interfaces in silicon

Color centers in host semiconductors are prime candidates as spin-photon interfaces for quantum applications. Finding an optimal spin-photon interface in silicon would move quantum information technologies toward a mature semiconducting host. However, the space of possible charged defects is vast, making the identification of candidates from experiments alone extremely challenging. Here, we use high-throughput first-principles computational screening to identify spin-photon interfaces among more than 1000 charged defects in silicon. The use of a single-shot hybrid functional approach is critical in enabling the screening of many quantum defects with a reasonable accuracy. We identify three promising spin-photon interfaces as potential bright emitters in the telecom band: Ti$^+_i$, Fe$^0_i$, and Ru$^0_i$. These candidates are excited through defect-bound excitons, stressing the importance of such defects in silicon for telecom band operations. Our work paves the way to further large-scale computational screening for quantum defects in semiconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Structure-Based Design of Small-Molecule Inhibitors of Human Interleukin-6

Human Interleukin-6 (hIL-6) is a pro inflammatory cytokine that binds to its receptor, IL-6Rα followed by binding to gp130 and subsequent dimerization to form a hexamer signaling complex. As a critical inflammation mediator, hIL-6 is associated with a diverse range of diseases and monoclonal antibodies in clinical use that either target IL-6Rα or hIL-6 to inhibit signaling. Here, we perform high-throughput structure-based computational screening using ensemble docking for small-molecule antagonists for which the target conformations were taken from 600 ns long molecular dynamics simulations of the apo protein. Prior knowledge of the contact sites from binary complex studies and experimental work was incorporated into the docking studies. The top 20 scoring ligands from the in silico studies after post analysis were subjected to in vitro functional assays. Among these compounds, the ligand with the second-highest calculated binding affinity experimentally showed an ~84% inhibitory effect on IL6-induced STAT3 reporter activity at 10 μM concentration. This finding may pave the way for designing small-molecule inhibitors of hIL-6 of therapeutic significance.

Human Interleukin-6↗
NASA NTRS1989

Strain-Layer-Superlattice Light Modulator

Conceptual device combines resonant reflection and photovoltaic action to enable one light beam to impose spatial and temporal modulation on another light beam. Such spatial light modulator, with high speed and multiplicity of parallel signal channels, used in image processing or similar computation requiring high data-throughput rates. Microstructures of GaAs and InAs with multiple quantum wells and compositional superlattices grown by molecular-beam epitaxy. Enhanced electro-optical properties of arrangement of alternating layers enables writing light beam to modulate reading light beam.

Maserjian, Joseph↗
DOE OSTI2022

Ab-initio Cu alloy design for high-gradient accelerating structures

Operation of normal conducting accelerator structures at high accelerating gradients is beneficial for many accelerator applications in basic science, industry, medicine, and National Security. RF breakdown is the major factor that limits the achievable accelerating gradients. Previous experiments on copper (Cu) have demonstrated that RF breakdown probability can be significantly decreased by hardening the material and alloying Cu with solutes such as silver (Ag). In this paper, we propose a figure-of-merit (FOM) that characterizes the ability of Cu alloys to withstand high-gradients. The FOM represents a trade-off between hardening through solid solution strengthening and the additional thermal stress induced by incremental RF pulse heating resulting from changes in electronic properties induced by alloying. We performed high-throughput ab initio calculations and computed the FOM for a large number of binary Cu alloys. Several promising candidate alloys for high-gradient accelerating structures were identified, such as CuAg, CuCd, CuHg, CuAu, CuIn, and CuMg. CuAg alloys have previously exhibited low RF breakdown rates in experiments. The results provide guidance for selecting alloys for the future high-gradient normal conducting accelerating structures operating at very high gradients.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-throughput search for magnetic topological materials using spin-orbit spillage, machine learning, and experiments

Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation. Here, we use systematic high-throughput density functional theory calculations to identify magnetic topological materials from the ≈ 40000 three-dimensional materials in the JARVIS-DFT database. First, we screen materials with net magnetic moment > 0.5 μB and spin-orbit spillage > 0.25, resulting in 25 insulating and 564 metallic candidates. The spillage acts as a signature of spin-orbit induced band-inversion. Then, we carry out calculations of Wannier charge centers, Chern numbers, anomalous Hall conductivities, surface bandstructures, and Fermi-surfaces to determine interesting topological characteristics of the screened compounds. We also train machine learning models for predicting the spillage, bandgaps, and magnetic moments of new compounds, to further accelerate the screening process. We experimentally synthesize and characterize a few candidate materials to support our theoretical predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1983

Parallelism and pipelining in high-speed digital simulators

The attainment of high computing speed as measured by the computational throughput is seen as one of the most challenging requirements. It is noted that high speed is cardinal in several distinct classes of applications. These classes are then discussed; they comprise (1) the real-time simulation of dynamic systems , (2) distributed parameter systems, and (3) mixed lumped and distributed systems. From the 1950s on, the quest for high speed in digital simulators concentrated on overcoming the limitations imposed by the so-called von Neumann bottleneck. Two major architectural approaches have made ig possible to circumvent this bottleneck and attain high speeds. These are pipelining and parallelism. Supercomputers, peripheral array processors, and microcomputer networks are then discussed.

Karplus, W. J.↗
DOE OSTI2026

pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers

Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code’s grid-decomposition approach scales nearly linearly with the number of graphics processing units (GPUs), even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global fast Fourier transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE (Atomic Simulation Environment) computational materials science Python packages, pyRMG automates input generation and convergence checking, and it integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. Here, we demonstrate pyRMG for a high-throughput study of strain effects in 2D 2L-Bi 2 Se 3 /2L-NbSe 2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.

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

Computational scanning tunneling microscope image database

We introduce the systematic database of scanning tunneling microscope (STM) images obtained using density functional theory (DFT) for two-dimensional (2D) materials, calculated using the Tersoff-Hamann method. It currently contains data for 716 exfoliable 2D materials. Examples of the five possible Bravais lattice types for 2D materials and their Fourier-transforms are discussed. All the computational STM images generated in this work are made available on the JARVIS-STM website ( https://jarvis.nist.gov/jarvisstm ). We find excellent qualitative agreement between the computational and experimental STM images for selected materials. As a first example application of this database, we train a convolution neural network model to identify the Bravais lattice from the STM images. We believe the model can aid high-throughput experimental data analysis. These computational STM images can directly aid the identification of phases, analyzing defects and lattice-distortions in experimental STM images, as well as be incorporated in the autonomous experiment workflows.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Constructing the Nitrogen Flux Maps (NFMs) of Plants

The main objectives of this project are to construct plant N flux maps (NFMs) from plant genomes and to determine functionality of AT enzymes and plant N metabolic network. To address this grand challenge, this project made use of rapidly growing numbers of plant genomes, high-throughput functional characterization platforms, and computational modeling to deduce both biochemical and systems level functionality of ATs and NFMs. The obtained NFMs will provide a novel framework to advance basic understanding of plant N metabolism and facilitate rational engineering of plants with high productivity even under limited N input.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

In situ visualization of multicomponents coevolution in a battery pouch cell

Lithium-ion battery (LIB) is a broadly adopted technology for energy storage. With increasing demands to improve the rate capability, cyclability, energy density, safety, and cost efficiency, it is crucial to establish an in-depth understanding of the detailed structural evolution and cell-degradation mechanisms during battery operation. Here, we present a laboratory-based high-resolution and high-throughput X-ray micro–computed laminography approach, which is capable of in situ visualizing of an industry-relevant lithium-ion (Li-ion) pouch cell with superior detection fidelity, resolution, and reliability. This technique enables imaging of the pouch cell at a spatial resolution of 0.5 μm in a laboratory system and permits the identification of submicron features within cathode and anode electrodes. We also demonstrate direct visualization of the lithium plating in the imaged pouch cell, which is an important phenomenon relevant to battery fast charging and low-temperature cycling. Our development presents an avenue toward a thorough understanding of the correlation among multiscale structures, chemomechanical degradation, and electrochemical behavior of industry-scale battery pouch cells.

25 ENERGY STORAGE↗