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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 OSTI2026

High-throughput oxidation screening and down-selection of refractory high entropy alloys in the Al-Cr-Mo-Nb-Ta-Ti system

Rapid experimentation and characterization are ever-present needs in the discovery of high entropy alloys. High entropy alloy systems are difficult to survey with systematic composition sweeps using traditional synthesis methods. The number of distinct compositions in even a four-element system is experimentally intractable. Exploration of these, and higher-element systems, necessitates thermodynamic prediction coupled with an automated sample creation method and a rapid screening methodology to effectively down-select alloys with targeted properties. As a result, a high-throughput method for evaluating the oxidation performance of refractory high entropy alloys was developed and tested. The six-element system of aluminum, chromium, molybdenum, niobium, tantalum, and titanium was evaluated for single phase stability and short-duration oxidation resistance. Target compositions were initially determined via thermodynamic predictions of single-phase stability across a wide temperature range. A twenty-five-sample build plate was produced using directed energy deposition additive manufacturing. After fabrication, the twenty-five 1 cm 3 samples were heat treated and characterized for composition and phase identification. The build plate was exposed to a high temperature oxidizing environment at 1000 °C for three hours. After oxidation, the composition, morphology, and chemistry of the oxides formed were characterized. Of the twenty-five samples produced, nine exhibited a favorable oxidation response, from which a single-phase BCC alloy at a composition of Al 13 Cr 7 Mo 19 Nb 18 Ta 26 Ti 17 was identified as the alloy with the most protective oxidation coating with a thin, adherent oxide scale. Finally, the complete experimental down-selection—from machine setup to final alloy identification—required approximately 45 labor hours, demonstrating a rapid validation for alloy discovery.

Additive manufacturing↗
DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗
DOE OSTI2024

Harnessing High‐Throughput Computational Methods to Accelerate the Discovery of Optimal Proton Conductors for High‐Performance and Durable Protonic Ceramic Electrochemical Cells

Abstract The pursuit of high‐performance and long‐lasting protonic ceramic electrochemical cells (PCECs) is impeded by the lack of efficient and enduring proton conductors. Conventional research approaches, predominantly based on a trial‐and‐error methodology, have proven to be demanding of resources and time‐consuming. Here, this work reports the findings in harnessing high‐throughput computational methods to expedite the discovery of optimal electrolytes for PCECs. This work methodically computes the oxygen vacancy formation energy (E V ), hydration energy (E H ), and the adsorption energies of H 2 O and CO 2 for a set of 932 oxide candidates. Notably, these findings highlight BaSn x Ce 0.8‐x Yb 0.2 O 3‐δ (BSCYb) as a prospective game‐changing contender, displaying superior proton conductivity and chemical resilience when compared to the well‐regarded BaZr x Ce 0.8‐x Y 0.1 Yb 0.1 O 3‐δ (BZCYYb) series. Experimental validations substantiate the computational predictions; PCECs incorporating BSCYb as the electrolyte achieved extraordinary peak power densities in the fuel cell mode (0.52 and 1.57 W cm −2 at 450 and 600 °C, respectively), a current density of 2.62 A cm −2 at 1.3 V and 600 °C in the electrolysis mode while demonstrating exceptional durability for over 1000‐h when exposed to 50% H 2 O. This research underscores the transformative potential of high‐throughput computational techniques in advancing the field of proton‐conducting oxides for sustainable power generation and hydrogen production.

08 HYDROGEN↗
DOE OSTI2023

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a “user guide” for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure–property relationships in soft materials.

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

Characterization of Performance Degradation Mechanisms in Low-Cost High Throughput DI-O3 Layer for Passivated Contact Silicon Solar Cells

Characterization and mitigating performance limiting defects in Silicon (Si) PV is one of key areas to be addressed to improve PV hardware costs and energy yield in order to lower the levelized cost of energy (LCOE) of installed PV cost to $0.02/kWh. As Si PV cells efficiencies have surpassed 22% and approaching 23%, the recombination at the metal contacts have become the focus point to be addressed. Passivated contact technologies—having a heterojunction with a band-gap larger than silicon between the metal and silicon—have emerged as a great potential for future highand ultrahigh-efficiency solar cells, as it concurrently reduces recombination and increases carrier selectivity, by incorporating thin films within the contact structure. Passivated contact Si solar cell technologies use a wide variety of tunnel layers—playing a crucial role to passivate metal contacts and tunnel charge carriers—including stoichiometric silicon oxide (SiO 2 ) grown by thermal oxidation and Low-Pressure Chemical Vapor Deposition (LPCVD) technique and silicon oxide (SiO x ) by hot nitric acid. However, thorough investigations on understanding the failure and performance degradation mechanisms associated with tunnel layers are still limited to date. Unlocking those degradation characteristics in crucial tunnel layers could improve the reliability and energy yield of passivated contact Si solar cells. Besides, the technique of growing aforementioned tunneling layers are low throughput, and requires high temperature processes and/or a vacuum environment. In this project, we investigated the performance degradation mechanisms of a low-cost high-throughput ozonated oxide (DI-O 3 ) tunnel layer for the passivated contact Si solar cells.

14 SOLAR ENERGY↗
DOE OSTI2021

A Ranked-Orbital Approach to Select Active Spaces for High-Throughput Multireference Computation

The past decade has seen a great increase in the application of high-throughput computation to a variety of important problems in chemistry. However, one area which has been resistant to the high-throughput approach is multireference wave function methods, in large part due to the technicalities of setting up these calculations and in particular the not always intuitive challenge of active space selection. As we look toward a future of applying high-throughput computation to all areas of chemistry, it is important to prepare these methods for large-scale automation. Here, we propose a ranked-orbital approach to select active spaces with the goal of standardizing multireference methods for high-throughput computation. This method allows for the meaningful comparison of different active space selection schemes and orbital localizations, and we demonstrate the utility of this approach across 1120 multireference calculations for the excitation energies of small molecules. Our results reveal that it is helpful to distinguish the method used to generate orbitals from the method of ranking orbitals in terms of importance for the active space. Additionally, we propose our own orbital ranking scheme that estimates the importance of an orbital for the active space through a pair-interaction framework from orbital energies and features of the Hartree–Fock exchange matrix. Here, we call this new scheme the “approximate pair coefficient” (APC) method and we show that it performs quite well for the test systems presented.

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

High-throughput electronic structure package

We introduce HTESP (High-Throughput Electronic Structure Package), an automated tool designed for high-throughput ab initio calculations. HTESP simplifies the entire workflow, including data extraction, input files generation, calculation submission, result collection, and plotting. The package is implemented in Python and Bash languages. In this paper, we provide detailed information about the package, its installation process and some illustrative examples to demonstrate its usage. Additionally, the package includes comprehensive online documentation on input parameters and tutorials to assist users.

Nepal, NirajK↗
DOE OSTI2026

High-throughput validation of phase formability and simulation accuracy of Cantor alloys

High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 °C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Photonic curing of solution-deposited ZrO 2 dielectric on PEN: a path towards high-throughput processing of oxide electronics

High-throughput manufacturing of oxide electronics will enable new applications ranging from large-area displays to flexible medical devices and low-cost solar panels. However, high quality oxide films from solution-based precursors typically require 20 minutes or more of thermal annealing at high temperature (> 250 °C) for each layer, severely limiting both the throughput and substrate choice. Here, we report high speed photonic curing of ZrO 2 dielectric thin films on flexible plastic substrates. The curing and patterning processes can be achieved simultaneously by using shadow mask patterning or adjusting conditions to convert oxide only on top of underlying metal contacts, i.e. self-aligned patterning. Metal-insulator-metal capacitors using two layers of ZrO 2 films photonically cured in just 100 seconds per layer show non-dispersive capacitance-frequency behavior from 10 2 to 10 6 Hz, high areal capacitance of 200 nF/cm 2 and low dissipation factor of 0.03 at 10 5 Hz, leakage current density of ~ 10 -7 A/cm 2 at an applied field of 2 MV/cm, and a breakdown field of nearly 8 MV/cm. Using an upgraded tool, similar dielectric properties are achieved in as short as 100 milliseconds using a single pulse of light, revealing a pathway to oxide film processing beyond 30 m/min.

14 SOLAR ENERGY↗
DOE OSTI2020

High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity

High-throughput and computational tools provide a new opportunity to calculate combined bioactivity of exposure to diverse chemicals acting through a common mechanism. We used high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) to estimate combined estrogen receptor (ER) agonist activity of non-pharmaceutical chemical exposures for the general U.S. population. High-throughput toxicokinetic (HTTK) data provide conversion factors that relate bioactive concentrations measured in vitro (µM), to predicted population geometric mean exposure rates (mg/kg/day). These data were available for 22 chemicals with ER agonist activity and were estimated for other ER bioactive chemicals based on the geometric mean of HTTK values across chemicals. For each chemical, ER bioactivity across ToxCast assays was compared to predicted population geometric mean exposure at different levels of in vitro potency and model certainty. Dose additivity was assumed in calculating a Combined Exposure-Bioactivity Index (CEBI), the sum of exposure/bioactivity ratios. Combined estrogen bioactivity was also calculated in terms of the percent maximum bioactivity of chemical mixtures in human plasma using a concentration-addition model. Estimated CEBIs vary greatly depending on assumptions used for exposure and bioactivity. In general, CEBI values were <1 when using median of the estimated general population chemical intake rates, while CEBI were ≥1 when using the upper 95th confidence bound for those same intake rates for all chemicals. Concentration-addition model predictions of mixture bioactivity yield comparable results. Based on current in vitro bioactivity data, HTTK methods, and exposure models, combined exposure scenarios sufficient to influence estrogen bioactivity in the general population cannot be ruled out. Future improvements in screening methods and computational models could reduce uncertainty and better inform the potential combined effects of estrogenic chemicals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Ultra-Fast Laser Fabrication of Alumina Micro-Sample Array and High-Throughput Characterization of Microstructure and Hardness

In the light of recent advances in material informatics, there is a great demand for high-throughput approaches of sample fabrication and property characterization. Currently, no high-throughput approach has been demonstrated for the fast sampling of the microstructure and the correlated properties. In this paper, we demonstrate the ultra-fast fabrication of an alumina sample array and the high-throughput hardness characterization of these sample units. The alumina sample array was fabricated using picosecond (PS) laser micromachining and CO2 laser sintering within a short time (i.e., less than a few minutes). After laser sintering, the hardness of these sample units was characterized using micro-indentation, and the microstructure was observed using scanning electron microscopy (SEM). In each sample unit, the microstructure was uniform for the entire top surface and within about 20 µm depth from the top surface. The relative density (RD) and corresponding micro-hardness of the sample units was found to continuously vary over a wide range from 89% RD with 600 kgf/mm2 hardness to 99% RD with 1609 kgf/mm2 hardness. For these laser-sintered samples, the correlation of hardness and relative density of the alumina matched well with the literature reports on sintered alumina obtained using conventional low-throughput furnace sintering experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High Throughput Source-less Plasma Deposition of Structured Silicon Anodes for Lithium-Ion Batteries

Amprius developed a manufacturing solution for silicon nanowire anode that relies on an inexpensive, high throughput, and high gas precursor utilization plasma deposition method that uses the anode foils as electrodes for plasma generation. The capacitively couple plasma (CCP) method is used in semiconductor and photovoltaic industry and Amprius modified existing high throughput equipment to use anode foils and to deposit amorphous silicon. The equipment was installed ahead of the program at Amprius site. The rest of the tasks included foil handling and process development. The equipment passed site acceptance tests (SAT) and the process parameter mapping was completed, indicating that the target process window limits produce output materials at the rate and with yield and specifications that meet the manufacturing target criteria. Amprius has hired supporting personnel to optimize processes and run the equipment. A parallel task verified the baseline performance of the silicon anode material, to be used as reference for the new manufacturing method.

25 ENERGY STORAGE↗
DOE OSTI2021

Development of a versatile, high-temperature, high-throughput ion irradiation system

We report ion irradiation has long been used as a surrogate for neutron irradiation experiments which are comparatively slower, more expensive, and often can leave materials activated and difficult to handle post-irradiation. However, with the growing use of combinatorial synthesis methods and high-throughput, automated characterization techniques, ion irradiation facilities will need to upgrade their capabilities to be able to accommodate these new material platforms. To address this challenge, a high-throughput ion irradiation system has been developed at the University of Wisconsin-Madison Ion Beam Laboratory (IBL). The system is capable of accommodating large sample arrays of arbitrary geometries and heat samples individually using an infrared laser to enable high-temperature, high-throughput ion irradiation while mitigating the risk of annealing the irradiation damage in nearby samples. To control stage motion, laser power, and beam current measurement, among other tasks, the program Chronos was developed which enables automated high-throughput irradiation experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

High Throughput Computational Framework of Materials Properties for Extreme Environments

This project aims to establish a framework capable of efficiently predicting the properties of structural materials for service in harsh environments over a wide range of temperatures and over long periods of time. The approach is to develop and integrate high throughput first-principles calculations in combination with machine learning (ML) methods, perform high throughput CALPHAD (calculations of phase diagrams) modeling, and carry out finite element method (FEM) simulations. Relevant to high temperature service in fossil power system, nickel-based superalloys such as Inconel 740 and Haynes 282 as well as the associated (Ni-Cr-Co)-Al-C-Fe-Mn-Mo-Nb-Si-Ti system, were investigated. The present framework was built on the concept of phase-based property data, in which properties of individual phases are modeled as a function of internal and external independent variables. This project established an open-source infrastructure with the following capabilities: (1) High throughput implementation of first-principles calculations at finite temperatures and variable compositions using both accurate phonon calculations and the efficient Debye model for thermodynamic properties, elastic constants, diffusion coefficients, vacancy formation, stacking and twin faults, and dislocation mobility; i.e., using the developed code DFTTK; (2) Machine learning capabilities to predict the above properties so that the number of first-principles calculations can be significantly reduced; e.g., using the developed code SIPFENN; (3) High throughput CALPHAD modeling of the above properties as a function of temperature and composition using our unique capability based on ESPEI and PyCalphad; (4) New capabilities to predict the stress-strain behavior of individual phases; and (5) New models for tensile strength prediction in common FEM software with the crystal plasticity finite element simulations (CPFEM).

, Ni-based superalloys↗
DOE OSTI2023

High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research

Solubility is crucial for redox flow batteries because it affects their energy density. A data-driven approach based on artificial intelligence/machine learning models can accelerate the development of highly soluble redox-active materials, but the lack of relevant, large-quantity data makes accurate solubility prediction difficult. To overcome this deficiency, we developed a high-throughput experimentation process that combines a robotically controlled platform with high-throughput methodology to collect large-scale and high-quality solubility data. We demonstrate the potential utility and applicability of this high-throughput process by measuring the aqueous and non-aqueous solubilities of redox-active materials and studying the effect of additives on their solubilities for both aqueous and non-aqueous redox flow battery applications. A redox flow battery based on our optimized negative electrolyte formulation and a ferrocyanide-positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.

25 ENERGY STORAGE↗
DOE OSTI2020

Use of high throughput screening methods to study dual-functional crosslinkable latexes

Herein, structure-formulation-property relationships for crosslinkable core-shell latexes were studied initially using high throughput screening methods and then using conventional experimental methods to determine optimum coating formulations. Two approaches that are used within coatings technology to improve sustainability are reduction in volatile organic compounds (VOC) and reduction in energy needed to cure coatings. Hybrid crosslinkable core-shell latexes have shown potential in meeting these challenges where film properties provided by particle coalescence are not sufficient and chemical crosslinking is needed. However, with numerous parameters involved such as latex particle architecture, nature and amount of crosslinker, and curing conditions, the screening of formulations via conventional coating testing methods becomes laborious and time-consuming. The results from high-throughput dye extraction and puncture testing were compared to data from conventional testing methods such as chemical resistance (MEK double rubs), pendulum hardness, and tensile testing to identify correlations. The dye extraction results have shown a strong correlation to solvent resistance and pendulum hardness data whereas toughness from puncture testing data correlated to tensile testing. Both high throughput dye extraction and puncture testing methods can be used for the accelerated screening of crosslinkable coating formulations to optimize curing parameters to achieve the best coating performance.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-throughput functional trait testing for bacterial pathogens

Functional traits are characteristics that affect the fitness and metabolic function of a microorganism. There is growing interest in using high-throughput methods to characterize bacterial pathogens based on functional virulence traits. Traditional methods that phenotype a single organism for a single virulence trait can be time consuming and labor intensive. Alternatively, machine learning of whole-genome sequences (WGS) has shown some success in predicting virulence. However, relying solely on WGS can miss functional traits, particularly for organisms lacking classical virulence factors. We propose that high-throughput assays for functional virulence trait identification should become a prominent method of characterizing bacterial pathogens on a population scale. This work is critical as we move from compiling lists of bacterial species associated with disease to pathogen-agnostic approaches capable of detecting novel microbes. We discuss six key areas of functional trait testing and how advancing high-throughput methods could provide a greater understanding of pathogens.

59 BASIC BIOLOGICAL SCIENCES↗