Computational Screening and Designing of Solid Materials for CO2 Capture Technology
Poster for American Physical Society (APS) March Meeting.
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Poster for American Physical Society (APS) March Meeting.
Two-piece screens have been designed specifically for use in filtering a granular material to remove contaminant particles that are significantly wider or longer than are the desired granules. In the original application for which the twopiece screens were conceived, the granular material is ammonium perchlorate and the contaminant particles tend to be wires and other relatively long, rigid strands. The basic design of the twopiece screens can be adapted to other granular materials and contaminants by modifying critical dimensions to accommodate different grain and contaminant- particle sizes. A two-piece screen of this type consists mainly of (1) a top flat plate perforated with circular holes arranged in a hexagonal pattern and (2) a bottom plate that is also perforated with circular holes (but not in a pure hexagonal pattern) and is folded into an accordion structure. Fabrication of the bottom plate begins with drilling circular holes into a flat plate in a hexagonal pattern that is interrupted, at regular intervals, by parallel gaps. The plate is then folded into the accordion structure along the gaps. Because the folds are along the gaps, there are no holes at the peaks and valleys of the accordion screen. The top flat plate and the bottom accordion plate are secured within a metal frame. The resulting two-piece screen is placed at the bottom opening of a feed hopper containing the granular material to be filtered. Tests have shown that such long, rigid contaminant strands as wires readily can pass through a filter consisting of the flat screen alone and that the addition of the accordion screen below the flat screen greatly increases the effectiveness of removal of wires and other contaminant strands. Part of the reason for increased effectiveness is in the presentation of the contaminant to the filter surface. Testing has shown that wire type contamination will readily align itself parallel to the material direction flow. Since this direction of flow is nearly always perpendicular to the filter surface holes, the contamination is automatically aligned to pass through. The two-filter configuration reduces the likelihood that a given contaminant strand will be aligned with the flow of material by eliminating the perpendicular presentation angle. Thus, for wires of a certain diameter, a two-piece screen is 20 percent more effective than is the corresponding flat perforated plate alone, even if the holes in the flat plate are narrower. An accordion screen alone is similarly effective in catching contaminants, but lumps of agglomerated granules of the desired material often collect in the valleys and clog the screen. The addition of a flat screen above the accordion screen prevents clogging of the accordion screen. Flat wire screens have often been used to remove contaminants from granular materials, and are about as effective as are the corresponding perforated flat plates used alone.
A flexible and extensible computational framework acts as a black-box materials discovery engine, capable of screening, predicting, and designing advanced materials with minimal manual intervention was developed. While developed for hydrogen sensing, the approach can be readily adapted to other materials challenges, offering a powerful tool for data-driven materials innovation.
To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.
Transport fuselage structures are designed to contain pressure following a large penetrating damage event. Applications of composites to fuselage structures require a database and supporting analysis on tension damage tolerance. Tests with 430 fracture specimens were used to accomplish the following: (1) identify critical material and laminate variables affecting notch sensitivity; (2) evaluate composite failure criteria; and (3) recommend a screening test method. Variables studied included fiber type, matrix toughness, lamination manufacturing process, and intraply hybridization. The laminates found to have the lowest notch sensitivity were manufactured using automated tow placement. This suggests a possible relationship between the stress distribution and repeatable levels of material inhomogeneity that are larger than found in traditional tape laminates. Laminates with the highest notch sensitivity consisted of toughened matrix materials that were resistant to a splitting phenomena that reduces stress concentrations in major load bearing plies. Parameters for conventional fracture criteria were found to increase with crack length for the smallest notch sizes studied. Most material and laminate combinations followed less than a square root singularity for the largest crack sizes studied. Specimen geometry, notch type, and notch size were evaluated in developing a screening test procedure. Traitional methods of correcting for specimen finite width were found to be lacking. Results indicate that a range of notch sizes must be tested to determine notch sensitivity. Data for a single small notch size (0.25 in. diameter) was found to give no indication of the sensitivity of a particular material and laminate layup to larger notch sizes.
Transport fuselage structures are designed to contain pressure following a large penetrating damage event. Application of composites to fuselage structures requires a data base and supporting analysis on tension damage tolerance. Tests with 430 fracture specimens were used to accomplish the following: (1) identify critical material and laminate variables affecting notch sensitivity, (2) evaluate composite failure criteria, and (3) recommend a screening test method. Variables studied included fiber type, matrix toughness, lamination manufacturing process, and intraply hybridization. The laminates found to have the lowest notch sensitivity were manufactured using automated tow placement. This suggests a possible relationship between the stress distribution and repeatable levels of material inhomogeneity that are larger than found in traditional tape laminates. Laminates with the highest notch sensitivity consisted of toughened matrix materials that were resistant to a splitting phenomena that reduces stress concentrations in load bearing plies. Parameters for conventional fracture criteria were found to increase with the crack length of the smallest notch sizes studied. Most materials and laminate combinations followed less than a square root singularity for the largest crack sizes studied. Specimen geometry, notch type, and notch size were evaluated in developing a screening test procedure. Results indicate that a range of notch sizes must be tested to determine notch sensitivity.
The results are reported of a preliminary feasibility screening study for providing long-term solutions to the problems of handling and managing radioactive wastes by extraterrestrial transportation of the wastes. Matrix materials and containers are discussed along with payloads, costs, and destinations for candidate space vehicles. The conclusions reached are: (1) Matrix material such as spray melt can be used without exceeding temperature limits of the matrix. (2) The cost in mills per kw hr electric, of space disposal of fission products is 4, 5, and 28 mills per kw hr for earth escape, solar orbit, and solar escape, respectively. (3) A major factor effecting cost is the earth storage time. Based on a normal operating condition design for solar escape, a storage time of more than sixty years is required to make the space disposal charge less than 10% of the bus-bar electric cost. (4) Based on a 10 year earth storage without further processing, the number of shuttle launches required would exceed one per day.
Here, we report the design, construction, and automation of a flat plate sample loading, alignment, and data acquisition system for X-ray diffraction measurements in reflection geometry implemented at the Stanford Synchrotron Radiation Lightsource. The system is built onto a single platform, enabling facile transferability, and is compartmentalized into sample storage, sample transfer, and sample position/alignment segments. The core feature of this system is a six-axis robotic arm that offers a large range of highly reproducible and programable movements. The degrees of freedom of the robot arm enable adaptability in which movements can be modified to fit various beamline environments and sample configurations. Samples are housed on 3D printed sample mounts, which are arranged onto a 6 × 2 array of sample cassettes capable of holding 7 samples. Using sample mounts designed for solid oxide electrolysis button cells (SOECs), the maximum tray capacity is 84 samples, which can be aligned and run in ~ 24 hours with long exposure scans. The sample array is additionally capable of accommodating a range of sample sizes and geometries due to the rapid 3D printed fabrication. The components of the setup will be described in detail and performance will be demonstrated with a set of representative SOEC and XRD standard samples. Opportunities for future developments and integration with the automated setup are summarized.
A systematic design-oriented, five-step approach to material selection is described: 1) establishing design requirements, 2) material screening, 3) ranking, 4) researching specific candidates and 5) applying specific cultural constraints to the selection process. At the core of this approach is the definition performance indices (i.e., particular combinations of material properties that embody the performance of a given component) in conjunction with material property charts. These material selection charts, which plot one property against another, are introduced and shown to provide a powerful graphical environment wherein one can apply and analyze quantitative selection criteria, such as those captured in performance indices, and make trade-offs between conflicting objectives. Finding a material with a high value of these indices maximizes the performance of the component. Two specific examples pertaining to aerospace (engine blades and pressure vessels) are examined, both at room temperature and elevated temperature (where time-dependent effects are important) to demonstrate the methodology. The discussion then turns to engineered/hybrid materials and how these can be effectively tailored to fill in holes in the material property space, so as to enable innovation and increases in performance as compared to monolithic materials. Finally, a brief discussion is presented on managing the data needed for materials selection, including collection, analysis, deployment, and maintenance issues.
Graph neural networks (GNNs) are a class of Deep Learning models used in designing atomistic materials for effective screening of large chemical spaces. To ensure robust prediction, GNN models must be trained on large volumes of atomistic data on leadership class supercomputers. Even with the advent of modern architectures that consist of multiple storage layers that include node-local NVMe devices in addition to device memory for caching large datasets, extreme-scale model training faces I/O challenges at scale.We present DDStore, an in-memory distributed data store designed for GNN training on large-scale graph data. DDStore provides a hierarchical, distributed, data caching technique that combines data chunking, replication, low-latency random access, and high throughput communication. DDStore achieves near-linear scaling for training a GNN model using up to 1000 GPUs on the Summit and Perlmutter supercomputers, and reaches up to a 6.15x reduction in GNN training time compared to state-of-the-art methodologies.
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.
Technical problems of design and flight qualification of the proposed classes of surface insulation materials and leading edge materials were reviewed. A screening test plan, a preliminary design data test plan and a design data test plan were outlined. This program defined the apparent critical differences between the surface insulators and the leading edge materials, structuring specialized screening test plans for each of these two classes of materials. Unique testing techniques were shown to be important in evaluating the structural interaction aspects of the surface insulators and a separate task was defined to validate the test plan. In addition, a compilation was made of available information on proposed material (including metallic TPS), previous shuttle programs, pertinent test procedures, and other national programs of merit. This material was collected and summarized in an informally structured workbook.
Efficient computation and visualization of elastic and mechanical properties are crucial in the selection of materials and the design of new materials. Here, the ElasTool v3.0 toolkit marks a significant advancement in the computational analysis and visualization of elastic and mechanical properties of materials, essential in material selection and design. This enhanced version extends beyond standard calculations like elastic tensor, Young's modulus, bulk modulus, and Poisson's ratio. It introduces capabilities for computing minimum thermal conductivity, linear compressibility, rendering the Christoffel equation, and elastic energy density. Notably, it integrates advanced visualization tools, including compatibility with Plotly and Elate web platforms for interactive web-based property exploration. A key feature of ElasTool v3.0 is the implementation of second-order elastic constants (SOECs) for tubular 2D-based nanostructures and nanotubes. Leveraging high-efficiency strain-matrix sets (OHESS), the toolkit now facilitates efficient computation of elastic constants and mechanical properties at both zero and finite temperatures for 1D, 2D, and 3D dimensions. ElasTool is openly accessible on GitHub: https://github.com/gmp007/elastool.
The influence of material volume on the transverse tensile strength of AS4/3501-6 graphite epoxy composites was investigated. Tensile tests of 90 degree laminates with 3 different widths and 5 different thicknesses were conducted. A finite element analysis was performed to determine the influence of the grip on the stress distribution in the coupons and explain the tendency for the distribution of failure locations to be skewed toward the grip. Specimens were instrumented with strain gages and extensometers to insure good alignment and to measure failure strains. Data indicated that matrix dominated strength properties varied with the volume of material that was stressed, with the strength decreasing as volume increased. Transverse strength data were used in a volumetric scaling law based on Weibull statistics to predict the strength of 90 degree laminates loaded in three point bending. Comparisons were also made between transverse strength measurements and out-of-plane interlaminar tensile strength measurements from curved beam bending tests. The significance of observed scale effects on the use of tests for material screening, quality assurance, and design allowables is discussed.
The Workmanship Standards serve to force positive outcomes rather than to depend only on finding and repairing defects. The Workmanship requirements address all avenues that can introduce defects or allow them to exist at the next level of integration: design, materials, processes, process control, screening.
The design and evaluation of built-up attachments and bonded joint concepts for use at elevated temperatures is documented. Joint concept screening, verification of GR/PI material, fabrication of design allowables panels, definition of test matrices, and analysis of bonded and bolted joints are among the tasks completed. The results provide data for the design and fabrication of lightly loaded components for advanced space transportation systems and high speed aircraft.
Shape memory alloys (SMAs) are a unique class of materials with several remarkable properties including shape recovery, superelasticity, etc. Especially important for many NASA applications is the ability to tune the martensitic phase transition temperature by varying the alloy composition. Nickel-titanium (NiTi) based alloys are the most widely studied of this class, with compositions involving ternary, quaternary, or higher additions being considered. Over the past several years, a significant database of SMA properties has been assembled by NASA researchers. Such a database is ideal for data science-based approaches including machine learning. We present results from a developed machine learning model capable of accurately predicting the transition temperature of SMAs across a wide range of compositions. Our model has the added benefit of interpretability and even provides confidence intervals for our predictions. This model will make rapid screening and design of new SMA materials possible. Predictions from the machine learning model can be validated by empirical and/or atomistic scale modeling.
The Single Stage To Orbit (SSTO) vehicle is designed to lift off from a vertical position, go into orbit, return to earth for a horizontal landing, and be reusable for the next mission. (NASA baseline only) In order to meet its performance goals, the SSTO relies on light weight structure and the use of 8 tri-propellant engines. These engines are mounted to the thrust structure. This test plan addresses selection of the material for this structure, and the integrity of the design through testing of elements and a full-scale subcomponent. This test plan supports the development of the design for an advanced composite thrust structure for a Single Stage to Orbit manned, heavy launch vehicle. The thrust structure is designed to transmit very high thrust loads from the engines to the rest of the vehicle (see Figure 1 ). The thrust structure will also be used for primary attachment of the twin vertical tails and possibly act as the aft attach point for the wing. The combination of high loading, high vibration, long service life and high acoustic environments will need to be evaluated by tests. To minimize design risk, a building block approach will be used. We will first screen materials to determine which materials show the most promise for this application. Factors in this screening will be the suitability of these materials for chosen design concepts, particularly concerning specific strength, environmental compatibility and applicability to fabrication processes. Next we will characterize two material systems that will be used in the design; the characterization will allow us to generate preliminary design data that will be used for the analysis. Element testing will be performed to evaluate critical structural locations under load. Final testing on the full scale test article will be performed to verify the design and to demonstrate predictability of the analysis. Additionally, risks associated with fabricating full scale thrust structures will be reduced through testing activities. One of the major concerns that stems from full scale fabrication is the realities of size and the associated complexities of handling, manufacturing, and assembly. The need exists to fabricate, assemble and test_representative joint specimens to achieve_confidence in the design and manufacturing technologies being proposed.