Machine Learning Assisted Screening of Two-Dimensional Materials for Water Desalination
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Chemistry↗
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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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High-throughput screening of 2D materials identifies p-type monolayer WS2 as potential ultra-high mobility semiconductor
Abstract 2D semiconductors offer a promising pathway to replace silicon in next-generation electronics. Among their many advantages, 2D materials possess atomically-sharp surfaces and enable scaling the channel thickness down to the monolayer limit. However, these materials exhibit comparatively lower charge carrier mobility and higher contact resistance than 3D semiconductors, making it challenging to realize high-performance devices at scale. In this work, we search for high-mobility 2D materials by combining a high-throughput screening strategy with state-of-the-art calculations based on the ab initio Boltzmann transport equation. Our analysis singles out a known transition metal dichalcogenide, monolayer WS 2 , as the most promising 2D semiconductor, with the potential to reach ultra-high room-temperature hole mobilities in excess of 1300 cm 2 /Vs should Ohmic contacts and low defect densities be achieved. Our work also highlights the importance of performing full-blown ab initio transport calculations to achieve predictive accuracy, including spin–orbital couplings, quasiparticle corrections, dipole and quadrupole long-range electron–phonon interactions, as well as scattering by point defects and extended defects.
Chemistry↗
Broad range material-to-system screening of metal–organic frameworks for hydrogen storage using machine learning
Hydrogen is pivotal in the transition to sustainable energy systems, playing major roles in power generation and industrial applications. Metal–organic frameworks (MOFs) have emerged as promising mediums for efficient hydrogen storage. However, identifying potential candidates for deployment is challenging due to the vast number of currently available synthesized MOFs. This study integrates molecular simulations, machine learning, and techno-economic analysis to evaluate the performance of MOFs across broad operation conditions for hydrogen storage applications. While previous screenings of MOF databases have predominantly emphasized high hydrogen capacities under cryogenic conditions, this study reveals that optimal temperatures and pressures for cost minimization depend on the raw price of the MOF. Specifically, when MOFs are priced at $15/kg, among the 9720 MOFs tested, 9692 MOFs achieve the lowest cost at temperatures between 170 K and 250 K and a pressure of 150 bar. Under these optimal conditions, 362 MOFs deliver a lower levelized cost of storage than 350 bar compressed gas hydrogen storage. Furthermore, this study reveals key material properties that result in low system cost, such as high surface areas (>3000 m2/g), large void fractions (>0.78), and large pore volumes (>1.1 cm3/g).
Hydrogen storage↗
Catalysis-in-a-Box: Robotic Screening of Catalytic Materials in the Time of COVID-19 and Beyond
Not Available
36 MATERIALS SCIENCE↗
Risks and reliability of manufacturing processes as related to composite materials for spacecraft structures
Fabricating primary aircraft and spacecraft structures using advanced composite materials entail both benefits and risks. The benefits come from much improved strength-to-weight ratios and stiffness-to-weight ratios, potential for less part count, ability to tailor properties, chemical and solvent resistance, and superior thermal properties. On the other hand, the risks involved include high material costs, lack of processing experience, expensive labor, poor reproducibility, high toxicity for some composites, and a variety of space induced risks. The purpose of this project is to generate a manufacturing database for a selected number of materials with potential for space applications, and to rely on this database to develop quantitative approaches to screen candidate materials and processes for space applications on the basis of their manufacturing risks including costs. So far, the following materials have been included in the database: epoxies, polycyanates, bismalemides, PMR-15, polyphenylene sulfides, polyetherimides, polyetheretherketone, and aluminum lithium. The first four materials are thermoset composites; the next three are thermoplastic composites, and the last one is is a metal. The emphasis of this database is on factors affecting manufacturing such as cost of raw material, handling aspects which include working life and shelf life of resins, process temperature, chemical/solvent resistance, moisture resistance, damage tolerance, toxicity, outgassing, thermal cycling, and void content, nature or type of process, associate tooling, and in-process quality assurance. Based on industry experience and published literature, a relative ranking was established for each of the factors affecting manufacturing as listed above. Potential applications of this database include the determination of a delta cost factor for specific structures with a given process plan and a general methodology to screen materials and processes for incorporation into the current conceptual design optimization of future spacecrafts as being coordinated by the Vehicle Analysis Branch where this research is being conducted.
Bao, Han P.↗
Evaluation of some candidate materials for automobile thermal reactors in engine-dynamometer screening tests
Fourteen materials were evaluated in engine screening tests on full-size thermal reactors for automobile engine pollution control systems. Cyclic test-stand engine operation provided 2 hours at 1040 C and a 20-minute air-cool to 70 C each test cycle. Each reactor material was exposed to 83 cycles in 200 hours of engine testing. On the basis of resistance to oxidation and distortion, the best materials included two ferritic iron alloys (Ge 1541 and Armco 18S/R), several commercial oxidation-resistant coatings on AlSl 651 (19-9 DL), and possibly uncoated AISI 310. The best commercial coatings were Cr-Al, Ni-Cr, and a glass ceramic.
Oldrieve, R. E.↗
Apparatus and methods for filtering granular solid material
Apparatuses for screening granular solid particulate material include a generally planar first screen and a second screen. A plurality of apertures extends through the first screen. At least a portion of the second screen is oriented at an angle to the first screen, and apertures extend through a perforated region of the second screen. The second screen includes at least one region configured to prevent at least some particles of solid material from passing through the second screen.
Backes, Douglas J.↗
Strategies to search for two-dimensional materials with long spin qubit coherence time
Two-dimensional (2D) materials that can host qubits with long spin coherence time (T 2 ) have the distinct advantage of integrating easily with existing microelectronic and photonic platforms, making them attractive for designing novel quantum devices with enhanced performance. However, the relative lack of 2D materials as spin qubit hosts, as well as appropriate substrates that can help maintain long T 2 , necessitates a strategy to search for candidates with robust spin coherence. Here, we develop a high-throughput computational workflow to predict the nuclear spin bath-driven qubit decoherence and T 2 in 2D materials and heterostructures. We initially screen 1172 2D materials and find 189 monolayers with T 2 > 1 ms, higher than that of naturally-abundant diamond. We then construct 1554 lattice-commensurate heterostructures between high-T 2 2D materials and select 3D substrates, and we find that T 2 is generally lower in a heterostructure than in the bare 2D host material; however, low-noise substrates (such as CeO 2 and CaO) can help maintain high T 2 . To further accelerate the material screening effort, we derive analytical models that enable rapid predictions of T 2 for 2D materials and heterostructures. The models offer a simple, yet quantitative, way to determine the relative contributions to decoherence from the nuclear spin baths of the 2D host and substrate in a heterostructural system. By developing a high-throughput workflow and analytical models, we expand the genome of 2D materials and their spin coherence times for the development of spin qubit platforms.
Toriyama, Michael Y. [Argonne National Laboratory ↗
Extending mARC II Arc-Jet Test Duration via Design and Implementation of Model System Cooling Sleeve
The mARC II is a 30 kW arc-jet facility at NASA Ames Research Center used to generate high-enthalpy flows for low-cost thermal protection system (TPS) technology development. Sustained operation of downstream instrumentation and material samples is constrained by thermal loading transmitted through the arc-jet test environment, limiting achievable run times and experimental throughput. This work presents the design, integration, and validation of a cooling sleeve implemented on the sweep arm drive motor feedthrough to mitigate thermal accumulation during testing. The addition of the cooling sleeve is a simple, robust upgrade that translates directly into enhanced facility capability by supporting longer run durations, reduced turnaround time, and higher throughput.
sting arm↗
Materials Design for Carbon Capture with Computational Screening and Machine Learning
Materials Design for Carbon Capture with Computational Screening and Machine Learning
Steckel, Janice↗
Computational Screening and Designing of Solid Materials for CO2 Capture Technology
Poster presentation on Computational Screening and Designing of Solid Materials for CO2 Capture Technology
Duan, Yuhua↗
Electro-Thermal Characterization of Dynamical VO 2 Memristors via Local Activity Modeling
We report translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO 2 /SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.
36 MATERIALS SCIENCE↗
A New Test Method for Material Flammability Assessment in Microgravity and Extraterrestrial Environments
The objective of this research is to modify the well-instrumented standard cone configuration to provide a reproducible bench-scale test environment that simulates the buoyant or ventilation flow that would be generated by or around a burning surface in a spacecraft or extraterrestrial gravity level. We will then develop a standard test method with pass-fail criteria for future use in spacecraft materials flammability screening. (For example, dripping of molten material will be an automatic fail.)
Olson, S. L.↗
BEAST DB: Grand-Canonical Database of Electrocatalyst Properties
We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze the electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain a granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage.
database↗
Materials selection for long life in LEO: A critical evaluation of atomic oxygen testing with thermal atom systems
The use of thermal atom test methods as a materials selection and screening technique for low-Earth orbit (LEO) spacecraft is critically evaluated. The chemistry and physics of thermal atom environments are compared with the LEO environment. The relative reactivities of a number of materials determined to be in thermal atom environments are compared to those observed in LEO and in high quality LEO simulations. Reaction efficiencies measured in a new type of thermal atom apparatus are one-hundredth to one-thousandth those observed in LEO, and many materials showing nearly identical reactivities in LEO show relative reactivities differing by as much as a factor of 8 in thermal atom systems. A simple phenomenological kinetic model for the reaction of oxygen atoms with organic materials can be used to explain the differences in reactivity in different environments. Certain specific thermal test environments can be used as reliable materials screening tools. Using thermal atom methods to predict material lifetime in LEO requires direct calibration of the method against LEO data or high quality simulation data for each material.
Koontz, S. L.↗
Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between
Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.
36 MATERIALS SCIENCE↗
Development of a polymetric grout for the hydrostatic bearing at DSS 14
Results of an investigation into the causes of the deterioration and premature failure of the grout under the hydrostatic bearing runner at DSS 14 are reported. Generic types of materials were screened and tested to find a grout material more resistive to the causes of grout failure. Emphasis was placed on the physical properties, strength, modulus of elasticity, and resistance to erosion and chemical attack by oil and unique requirements imposed by each material for mixing, placing, compacting, and cooling. The polymetric grout developed to replace the dry grout is described.
Mcclung, C. E.↗
On the Use of Accelerated Aging Methods for Screening High Temperature Polymeric Composite Materials
A rational approach to the problem of accelerated testing of high temperature polymeric composites is discussed. The methods provided are considered tools useful in the screening of new materials systems for long-term application to extreme environments that include elevated temperature, moisture, oxygen, and mechanical load. The need for reproducible mechanisms, indicator properties, and real-time data are outlined as well as the methodologies for specific aging mechanisms.
Gates, Thomas S.↗