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At least 793 records · Page 44

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Equilipy: a python package for calculating phase equilibria

The CALPHAD (CALculation of PHAse Diagram) approach (Nigel Saunders & Miodownik, 1998) provides predictions for thermodynamically stable phases in multicomponent-multiphase materials across a wide range of temperatures. Consequently, the CALPHAD calculations became an essential tool in materials and process design (Luo, 2015). Such design tasks frequently require navigating a high-dimensional space due to multiple components involved in the system. This increasing complexity demands high-throughput CALPHAD calculations, especially in the rapidly evolving field of alloy design. In response to the need, we developed Equilipy an open-source Python package designed for calculating phase equilibria of multicomponent-multiphase systems. Equilipy is specifically tailored for high-throughput CALPHAD calculations, offering parallel computations across multiple processors and nodes with the given NPT input conditions namely elemental compositions (N), pressure (P), and temperature (T). Equilipy utilizes the program structure and Gibbs energy functions from the Fortran-based program, Thermochimica (Piro et al., 2013), with incorporating a new Gibbs energy minimization algorithm. This algorithm, originally developed by Capitani and Brown in 1987 (Capitani & Brown, 1987), has been revised and implemented to enhance the stability and performance of calculations. The Fortran codes are precompiled and interfaced with Python via F2PY, ensuring high computation speed. Benchmark tests shown in Figure 1 demonstrate that Equilipy’s computation speed is comparable to those of established commercial software, TC-Python and PanPython. This result highlights its efficiency and potential applications in various scientific and industrial fields.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

A dual dynamic shutter system for accelerating ion irradiation sample throughput via lateral gas implantation gradients

Ion irradiation for material performance testing is limited due to its serial nature, which allows for only one value of the implantation (appm) versus dose (dpa) parameter space to be explored for each ion and experiment at a time. While ion irradiation can accelerate the process by up to three orders of magnitude compared to neutron irradiation experiments, the sample throughput for ion irradiation remains relatively low. To address these limitations, a novel capability has been developed at the Michigan Ion Beam Laboratory (MIBL), enabling for the creation of single- and two-dimensional lateral ion implantation gradients using recently installed motorized-controlled ion-beam shutters. This advancement can generate a wide scope of the two-dimensional (H+, He2+) implantation parameter space within a single sample. Integration of this new capability now allows for dual- and triple-ion beam experiments to be performed with full user control over not only the ion implantation depth, but also laterally across the sample by imposing ion implantation concentration gradients, thus providing researchers with a high-throughput means for material testing under various irradiation conditions. Furthermore, recent improvements in MIBL's microbeam ion-beam analysis (IBA) target station now allow for probing these concentration gradients in irradiated alloys with exceptional spatial resolution, down to 10 µm. These two approaches promise to significantly improve ion irradiation capabilities and increase the sample throughput by several orders of magnitude. The application of the shutter technique plus the subsequent microbeam characterization of the imposed implantation gradients are showcased by two proof-of-principle ion-irradiated experiments, one performed on single-crystal Si and the other on the fusion-candidate alloy F82H-IEA. These advancements mark a substantial leap in ion-beam technology, offering researchers a robust, high-throughput method to efficiently investigate candidate alloys with high technological readiness for both advanced fission and fusion reactor applications, in a time- and cost-effective manner.

36 - MATERIALS SCIENCE↗

NASA NTRS2004

Plasma Enhanced Growth of Carbon Nanotubes For Ultrasensitive Biosensors

The multitude of considerations facing nanostructure growth and integration lends itself to combinatorial optimization approaches. Rapid optimization becomes even more important with wafer-scale growth and integration processes. Here we discuss methodology for developing plasma enhanced CVD growth techniques for achieving individual, vertically aligned carbon nanostructures that show excellent properties as ultrasensitive electrodes for nucleic acid detection. We utilize high throughput strategies for optimizing the upstream and downstream processing and integration of carbon nanotube electrodes as functional elements in various device types. An overview of ultrasensitive carbon nanotube based sensor arrays for electrochemical bio-sensing applications and the high throughput methodology utilized to combine novel electrode technology with conventional MEMS processing will be presented.

Cassell, Alan M.↗

DOE OSTI2025

A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties

Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose an approach for building self-supervised autonomy into contact-based robotic systems that teach the robot to follow domain expert measurement principles at high throughputs. We demonstrate the performance of this approach by autonomously driving a 4-DOF robotic probe for 24 hours to characterize semiconductor photoconductivity at 3025 uniquely predicted poses across a gradient of drop-casted perovskite film compositions, achieving throughputs of more than 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals compositional trends and regions of inhomogeneity, valuable for identifying manufacturing defects. With this self-supervised neural network–driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories.

Science & Technology - Other Topics↗

DOE OSTI2024

Grand canonically optimized grain boundary phases in hexagonal close-packed titanium

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness

Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. Here, in this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R 2 > 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies.

36 MATERIALS SCIENCE↗

NASA NTRS2015

High-Reflectivity Multi-Layer Coatings for the CLASP Sounding Rocket Project

We are planning an international rocket experiment Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) is (2015 planned) that Lyman alpha line (Ly alpha line) polarization spectroscopic observations from the sun. The purpose of this experiment, detected with high accuracy of the linear polarization of the Ly alpha lines to 0.1% by using a Hanle effect is to measure the magnetic field of the chromosphere-transition layer directly. For polarization photometric accuracy achieved that approximately 0.1% required for CLASP, it is necessary to realize the monitoring device with a high throughput. On the other hand, Ly alpha line (vacuum ultraviolet rays) have a sensitive characteristics that is absorbed by the material. We therefore set the optical system of the reflection system (transmission only the wavelength plate), each of the mirrors, subjected to high efficiency of the multilayer coating in accordance with the role. Primary mirror diameter of CLASP is about 30 cm, the amount of heat about 30,000 J is about 5 minutes of observation time is coming mainly in the visible light to the telescope. In addition, total flux of the sun visible light overwhelmingly large and about 200 000 times the Ly alpha line wavelength region. Therefore, in terms of thermal management and 0.1% of the photometric measurement accuracy achieved telescope, elimination of the visible light is essential. We therefore, has a high reflectivity (greater than 50%) in Ly alpha line, visible light is a multilayer coating be kept to a low reflectance (less than 5%) (cold mirror coating) was applied to the primary mirror. On the other hand, the efficiency of the polarization analyzer required chromospheric magnetic field measurement (the amount of light) Conventional (magnesium fluoride has long been known as a material for vacuum ultraviolet (MgF2) manufactured ellipsometer; Rs = 22%) about increased to 2.5 times were high efficiency reflective polarizing element analysis. This device, Bridou et al. (2011) is proposed "that is coated with a thin film of the substrate MgF2 and SiO2 fused silica." As a result of the measurement, Rs = 54.5%, to achieve a Rp = 0.3%, high efficiency, of course, capable of taking out only about spolarized light. Other reflective optical elements (the secondary mirror, the diffraction grating-collector mirror), subjected to high-reflection coating of Al + MgF2 (reflectance of about 80%), less than 5% in the entire optical system by these (CCD Science was achieved a high throughput as a device for a vacuum ultraviolet ray of the entire system less than 5% (CCD of QE is not included).

sounding rocket↗

NASA NTRS2015

Solar Lyman-Alpha Polarization Observation of the Chromosphere and Transition Region by the Sounding Rocket Experiment CLASP

We are planning an international rocket experiment Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) is (2015 planned) that Lyman α line (Ly(alpha) line) polarization spectroscopic observations from the sun. The purpose of this experiment, detected with high accuracy of the linear polarization of the Ly(alpha) lines to 0.1% by using a Hanle effect is to measure the magnetic field of the chromosphere-transition layer directly. For polarization photometric accuracy achieved that approx. 0.1% required for CLASP, it is necessary to realize the monitoring device with a high throughput. On the other hand, Ly(alpha) line (vacuum ultraviolet rays) have a sensitive characteristics that is absorbed by the material. We therefore set the optical system of the reflection system (transmission only the wavelength plate), each of the mirrors, subjected to high efficiency of the multilayer coating in accordance with the role. Primary mirror diameter of CLASP is about 30 cm, the amount of heat about 30,000 J is about 5 minutes of observation time is coming mainly in the visible light to the telescope. In addition, total flux of the sun visible light overwhelmingly large and about 200 000 times the Ly(alpha) line wavelength region. Therefore, in terms of thermal management and 0.1% of the photometric measurement accuracy achieved telescope, elimination of the visible light is essential. We therefore, has a high reflectivity (> 50%) in Lyα line, visible light is a multilayer coating be kept to a low reflectance (<5%) (cold mirror coating) was applied to the primary mirror. On the other hand, the efficiency of the polarization analyzer required chromospheric magnetic field measurement (the amount of light) Conventional (magnesium fluoride has long been known as a material for vacuum ultraviolet (MgF2) manufactured ellipsometer; Rs = 22%) about increased to 2.5 times were high efficiency reflective polarizing element analysis. This device, Bridou et al. (2011) is proposed "that is coated with a thin film of the substrate MgF2 and SiO2 fused silica." As a result of the measurement, Rs = 54.5%, to achieve a Rp = 0.3%, high efficiency, of course, capable of taking out only about s-polarized light. Other reflective optical elements (the secondary mirror, the diffraction gratingcollector mirror), subjected to high-reflection coating of Al + MgF2 (reflectance of about 80%), less than 5% in the entire optical system by these (CCD Science was achieved a high throughput as a device for a vacuum ultraviolet ray of the entire system less than 5% (CCD of QE is not included).

Narukage, Noriyuki↗

DOE OSTI2023

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗

DOE OSTI2020

Phase evolution and amorphous stability upon solid-state reaction in superlattice like Ge–Sb–Te combinatorial thin-film

In this paper, the superlattice-like (SLL) Ge–Sb–Te combinatorial thin films were prepared by using a high-throughput ion beam sputtering system. The phase evolution and amorphous stability of such films undergoing heat treatment as a function of the coating sequence and modulation period were systematically studied. The composition structure diagram was constructed via an automated process of data obtained by high-throughput synchrotron micro-X-ray diffraction and lab-based micro-X-ray fluorescence. Furthermore, the element distribution and microstructure in the depth direction of the SLL thin films were characterized with time-of-flight secondary ion mass spectrometry and transmission electron microscopy, respectively. These studies demonstrated that the coating sequence has a significant effect on the element distribution in the as-deposited SLL thin films and the structure of the final product upon solid-state reaction. Reducing the modulation period of the SLL thin film improves the stability of the amorphous Ge–Sb–Te phase. This work lays a solid foundation for the rational design of SLL Ge–Sb–Te thin films to improve their performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Multiplication of freestanding III-V semiconductor membranes from a single wafer by alternating growth with amorphous graphene interlayer

Freestanding single-crystalline III-V compound semiconductors are important building blocks for functional devices due to their high electron mobilities, a wide range of bandgaps, and excellent optoelectronic properties. Despite efforts to produce such membranes by detaching epitaxial layers from donor wafers, current methods suffer from either slow processes or poor material quality. Here, we demonstrate a technique to grow and harvest multiple epitaxial membranes with extremely high throughput at the wafer scale. For this, a process to directly grow amorphous graphene on III-V substrates in metal-organic chemical vapor deposition reactors is developed, which enables an advanced remote epitaxy scheme comprised of multiple alternating layers of amorphous graphene and III-V epitaxial layers that can be formed by a single epitaxy run. Each epilayer in the multi-stack structure is then harvested by layer-by-layer peeling, producing multiple freestanding membranes with unprecedented throughput from a single wafer. Because amorphous graphene provides a weak van der Waals interface that allows peeling at the interface without damaging the epilayer or the substrate, wafers can be reused for subsequent membrane production. Therefore, this work represents a meaningful step toward high-throughput and low-cost production of single-crystal membranes that can be heterointegrated.

Han, Ne Myo↗

DOE OSTI2022

Multiplication of freestanding III-V semiconductor membranes from a single wafer by alternating growth with amorphous graphene interlayer

Han, Ne Myo↗

DOE OSTI2024

Bragg glass signatures in Pd x ErTe 3 with X-ray diffraction temperature clustering

The Bragg glass phase is a nearly perfect crystal with glassy features predicted to occur in vortex lattices and charge-density-wave systems in the presence of disorder. Detecting it has been challenging, despite its sharp theoretical definition in terms of diverging correlation lengths. Here we present bulk probe evidence supporting a Bragg glass phase in the systematically disordered charge-density-wave material of Pd x ErTe 3 . We do this by using comprehensive X-ray data and a machine-learning-based analysis tool called X-ray diffraction temperature clustering (X-TEC). We establish a diverging correlation length in samples with moderate intercalation over a wide temperature range. To enable this analysis, we introduced a high-throughput measure of inverse correlation length that we call peak spread. The detection of Bragg glass order and the resulting phase diagram advance our understanding of the complex interplay between disorder and fluctuations. Moreover, the use of our analysis technique to target fluctuations through a high-throughput measure of peak spread can revolutionize the study of fluctuations in scattering experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Enhanced Configurational Sampling Approaches to Alchemical Ligand Binding Free Energy Simulations: Current Status and Challenges

In this study, ligand binding free energy simulations (LB-FES) have been routine tasks in modern drug discovery campaign. A long-standing challenge for LB-FES is the difficulty in adequately sampling nontrivial environmental reorganizations in response to ligand binding. Therefore, various enhanced configurational sampling (ECS) approaches were devised to speed up fluctuations of relevant slow degrees of freedom (SDOF) and ensure simulation convergence. However, in contrast to the achievements in parametrization, software performance, and workflow automation, efficient ECS methodology suitable for high throughput screening remains in an early stage of development. Here, a review of ECS developments with LB-FES is presented, revisiting current approaches and underlining the major technical pitfalls and challenges. This Perspective focuses on alchemical LB-FES on account of their predominant role in high throughput drug screening as well as the established partnership with ECS. The critical aspects of designing ECS approaches, from both theoretical and applied perspectives, are described. This work is intended to provide a contemporary review of the scientific, technical, and practical issues associated with the accelerating convergence of alchemical LBFES.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Developing machine learning for heterogeneous catalysis with experimental and computational data

Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyze trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R 2 values, we compare the performances based on these mentioned trends.

Computational chemistry↗

DOE OSTI2022

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Chemistry↗

NASA NTRSDate not supplied

Overview and Performance Characterization of Northrop Grumman’s 1 kW Hall Thruster String

Northrop Grumman (NG) Tactical Space SystemsDivisionhas embarked on the development and qualification of a high throughput, low power Hall Thruster String (HTS) using hardware designed and built in-house.Following the success of Mission Extension Vehicles 1 and 2, NG is currently developing the next generation in its lineupof satellite servicing capabilities, the Mission Robotics Vehicle(MRV)and Mission Extension Pod (MEP).MEP’s mission profile imposes highly demanding requirements upon the electric propulsion system. When surveying the industry for available systems, NGwas unable to identify any mature Hall thrustersystems that could satisfy the performance and lifetime requirements for MEP. Eventually, it was decided to vertically integrate the EP development process, partneringwith NASA Glenn Research Center to leverage ongoing development at GRC of a high throughput, low power Hall thruster. The components of the HTS have successfully passed PDR and are currently in the engineering development and test phase. Based on performancecharacterization testingof the development hardware,NG’s low power Hall thruster, dubbed the NGHT-1X, promises to deliver state-of-the-art performanceand lifetime for a sub-kW Hall thruster, achieving total efficiencies of 50-55% over a wide range of throttle conditions. Results of a seriesof characterization tests including integrated systems testingwith the PPUare presented.Qualification methodology andthruster lifetime verification is also discussed. Environmental qualification of the HTS components is expected to completein mid-2023 with a first flight in mid-2024.

Propulsion↗

DOE OSTI2020

Multiplexed binary assembly and quantitative tracking of bacterial populations

Compositions and methods for tracking combinations of mutations in populations by coupling DNA assembly and paired-end sequencing technology with high-throughput sequencing. This allows for an unlimited number of mutation sites in a population of microorganisms to be identified, and allows sites to be sequenced across several replicates. Unique identifiers (DNA barcodes) can be used to measure fitness data by sequencing of the barcodes multiplexed with the mutation sites with high-throughput short sequencing read technology, allowing selections to be rapidly performed on populations of known combinatorial genotypes.

59 BASIC BIOLOGICAL SCIENCES↗