Materials Design for Carbon Capture with Computational Screening and Machine Learning
Materials Design for Carbon Capture with Computational Screening and Machine Learning
Steckel, Janice↗
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Results for “Materials Design”
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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Higher-order factorization machine for accurate surrogate modeling in material design
Efficient and robust optimization is important in material science for identifying optimal structural parameters and enhancing material performance. Surrogate-based active learning algorithms have recently gained great attention for their ability to efficiently navigate large, high-dimensional design spaces. Among surrogate models, 2 nd -order factorization machine (FM) models are widely employed as the surrogate model in active learning algorithms due to their balance between simplicity and effectiveness. However, their quadratic nature limits their capacity to capture complex, higher-order interactions among variables, often leading to suboptimal solutions. To overcome this limitation, we propose an active learning scheme integrating a 3 rd -order FM model, capable of modeling three-variable interactions and more intricate relationships in material systems. We comprehensively evaluate the surrogate modeling performance of the 3 rd -order FM case using various objective functions. Furthermore, we examine the optimization reliability and efficiency of the 3 rd -order FM-based active learning in a real-world material design task (e.g., nanophotonic structures for transparent radiative cooling). Our study shows that the 3 rd -order FM outperforms the 2 nd -order model in both surrogate accuracy and optimization performance, highlighting higher-order models’ promises for material design and optimization problems.
Factorization machine↗
Evaluating Material Design Principles for Calcium-Ion Mobility in Intercalation Cathodes
Multivalent-ion batteries offer an alternative to Li-based technologies, with the potential for greater sustainability, improved safety, and higher energy density, primarily due to their rechargeable system featuring a passivating metal anode. Although a system based on the Ca 2+ /Ca couple is particularly attractive given the low electrochemical plating potential of Ca 2+ , the remaining challenge for a viable rechargeable Ca battery is to identify Ca cathodes with fast ion transport. In this work, a high-throughput computational pipeline is adapted to (1) discover novel Ca cathodes in a largely unexplored space of empty intercalation hosts and (2) develop material design rules for Ca-ion mobility. One candidate from the screening, W 2 O 3 (PO 4 ) 2 , is confirmed to have a low Nudged Elastic Band (NEB) barrier of 168 meV within a one-dimensional (1D) ion percolation topology. This candidate is subsequently synthesized and electrochemically tested, achieving reversible Ca cycling with a capacity of 25 mA h/g. To further accelerate the screening for promising Ca intercalation electrodes, machine learning (ML) Random Forest (RF) and Extreme Gradient Boosting (XGB) classification models are created with local environment descriptors based on a large, structurally and chemically diverse dataset of minimum energy pathways, spanning over 5,000 density functional theory (DFT) site energy calculations. Accuracies of 92% are achieved, material design metrics are quantified, ML force-fields are leveraged in an accelerated iteration of the screening, and a total of 27 novel Ca cathode materials are highlighted for further investigation.
25 ENERGY STORAGE↗
Uncertainty-aware mixed-variable machine learning for materials design
Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.
36 MATERIALS SCIENCE↗
Revolutionizing Materials Design: The Intersection of Quantum Mechanics and Data Modeling
The field of materials design is currently experiencing a notable evolution, driven by the convergence of sophisticated computational methodologies based on first principles and data-driven modeling approaches. I will review our recent endeavors employing AI/ML to expedite first-principles simulations and mitigate traditional methods' temporal and spatial limitations. Central to our efforts is developing and utilizing ML interatomic potentials (MLPs) across a diverse spectrum of materials. We show that MLPs serve as invaluable tools for navigating the complexities of the simulations, such as understanding the behavior of MgO at extreme environments of ~1 terapascal and temperatures >10,000 Kelvin. Moreover, we show that MLPs can provide precise details of the intricate dynamics governing the oxidation processes of binary alloy systems due to the competition between surface segregation and reconstruction tendencies. In summation, advancements in MLPs open the door to fresh possibilities in material modeling and, ultimately, discovery.
Saidi, Wissam↗
Safety Standard for Hydrogen and Hydrogen Systems: Guidelines for Hydrogen System Design, Materials Selection, Operations, Storage and Transportation
The NASA Safety Standard, which establishes a uniform process for hydrogen system design, materials selection, operation, storage, and transportation, is presented. The guidelines include suggestions for safely storing, handling, and using hydrogen in gaseous (GH2), liquid (LH2), or slush (SLH2) form whether used as a propellant or non-propellant. The handbook contains 9 chapters detailing properties and hazards, facility design, design of components, materials compatibility, detection, and transportation. Chapter 10 serves as a reference and the appendices contained therein include: assessment examples; scaling laws, explosions, blast effects, and fragmentation; codes, standards, and NASA directives; and relief devices along with a list of tables and figures, abbreviations, a glossary and an index for ease of use. The intent of the handbook is to provide enough information that it can be used alone, but at the same time, reference data sources that can provide much more detail if required.
Source record↗
Safety Standard for Oxygen and Oxygen Systems: Guidelines for Oxygen System Design, Materials Selection, Operations, Storage, and Transportation
NASA's standard for oxygen system design, materials selection, operation, and transportation is presented. Minimum guidelines applicable to NASA Headquarters and all NASA Field Installations are contained.
Source record↗
Accelerating Materials Design and Synthesis Using AI/ML at NETL
Presentation about AI/ML capabilities in materials design and synthesis.
Saidi, Wissam↗
Multi-Scale Materials Design for Inflight Icing Mitigation
Future ice protection system development needs to integrate product and material design in a computational environment in order to analyze and enhance system performance.
rotorcraft↗
JARVIS-Leaderboard: a large scale benchmark of materials design methods
Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/
36 MATERIALS SCIENCE↗
Accelerated Materials Design for Molten Salt Technologies Using Innovative High-Throughput Methods
The focus of the project is on building an innovative accelerated materials design platform for molten salt technologies using novel high-throughput methods coupled to data analytics. The main objectives is to predict a FeCrMnNi alloy compositional space with better corrosion resistance than stainless steel 316 and identify new molten salt corrosion mechanisms. The project demonstrates the feasibility to use high-throughput methods coupled to data analytics to accelerate alloy design for extreme environments applications. Using a trained and tested machine learning (ML) model, 2000 FeCrMnNi alloy corrosion rate in molten chloride salts were predicted and a compositional field with corrosion rate lower than 316 stainless steel was identified. The ML model interpretability unveiled multiple features of importance in the model prediction. Some features were expected to be of relative significance, such as work function, surface energy and alloy electronegativity, and the ML model interpretability analysis confirmed those. On the other hand, the most important feature is the diffusion coefficient of Ni in the bulk alloy which indicates that a surface diffusion mechanism plays an important role in the overall corrosion mechanism in molten salts.
36 MATERIALS SCIENCE↗
High-Entropy Metal-Organic Frameworks (HEMOFs): A New Frontier in Materials Design for CO 2 Utilization
High-entropy materials (HEMs) emerged as promising candidates for a diverse array of chemical transformations, including CO 2 utilization. However, traditional HEMs catalysts are nonporous, limiting their activity to surface sites. Designing HEMs with intrinsic porosity can open the door toward enhanced reactivity while maintaining the many benefits of high configurational entropy. Here, in this study, a synergistic experimental, analytical, and theoretical approach to design the first high-entropy metal-organic frameworks (HEMOFs) derived from polynuclear metal clusters is implemented, a novel class of porous HEMs that is highly active for CO 2 fixation under mild conditions and short reaction times, outperforming existing heterogeneous catalysts. HEMOFs with up to 15 distinct metals are synthesized (the highest number of metals ever incorporated into a single MOF) and, for the first time, homogenous metal mixing within individual clusters is directly observed via high-resolution scanning transmission electron microscopy. Importantly, density functional theory studies provide unprecedented insight into the electronic structures of HEMOFs, demonstrating that the density of states in heterometallic clusters is highly sensitive to metal composition. This work dramatically advances HEMOF materials design, paving the way for further exploration of HEMs and offers new avenues for the development of multifunctional materials with tailored properties for a wide range of applications.
36 MATERIALS SCIENCE↗
Biomaterials Out of Thin Air: In Situ, On-Demand Printing of Advanced Biocomposites: A New Materials Design and Production Technique Using 3D-Printed Arrays of Bioengineered Cells
We have completed the proof of concept described in our Phase I proposal, a two-material array of nonstructural proteins. We created an implementation of each step in our technology concept and demonstrated its critical functionality. The biological chassis and printing hardware we created as part of this work can be re-used for future work by inserting a material coding region upstream of the fluorescent tag. Overall, we showed that our technology concept is sound. The mission benefit analyses, as described in our Phase I proposal, are complete and contained in this report. These calculations show that our technology can save hundreds of kilograms of upmass for a potential planetary human habit construction mission: the mass per habitat module can be reduced by approximately one third if the biomaterials are manufactured on Earth and included in the mission upmass, and the full 240 kg per module can be saved if the materials are derived entirely from in situ resources. Mass savings between these two extremes is expected for an actual mission, depending on the level of in situ resource extraction technology. We have shown that continued advancement of this technology concept for use in a space mission environment is justified. Our survey of future development pathways proved extremely informative in light of the lessons learned from our proof of concept work and mission scenario analyses. For example, we were able for the first time to distinguish between the levels of functionality provided by production of structural proteins, other polymers such as polysaccharides, and true organic-inorganic composites such as bone and mineralized shell. This new information represents a significant advance in formulating specific applications, and key enabling technologies, for our proposed concept. We surveyed potential collaborations with other projects and synergies with enabling technologies that are developing. We have received requests for collaboration from other institutions, including labs at Stanford University and Drexel University. We have also received visits from industry, including Organovo, a tissue engineering company, and Autodesk, a major 3D and materials design software company. Finally, we have been in touch with the team behind the 2013 NIAC Phase ll 'Super Ball Bot-Structures for Planetary Landing and Exploration' and are planning to develop our biomaterial printing technology with the goal of enabling tensegrity-based rovers such as theirs to use lighter, more robust materials. A smooth transition from TRL 2 to TRL 3 assumes that the implementations of the technology concept which demonstrate critical functionality are also pathways for future development; while this is the case for most hardware or software projects, the multidisciplinary nature of our project, particularly the biological aspect of it, means that this is not always true. For example, as part of this work we showed that although there are large number of known genetic parts that correspond to non-structural materials, this is not true for sequences for structural organic proteins, let alone biominerals. These realizations allowed us to further subdivide our concept into more detailed development areas, some of which are clearly established at TRL 3, others of which were newly identified sub-technologies moved from TRL 1 to TRL 2. Similarly, although a single feasibility /benefit analysis is sufficient for advancement from TRL 2 to TRL 3, not all potential benefits to a technology concept as broad in scope as ours are apparent at TRL 2. Both our future pathways survey and our proof of concept work highlighted that the true mass savings potential of our technology concept cannot be quantified without modification of existing materials modelling tools to take into account the possibility of positional materials properties customization. Therefore, we have simultaneously both advanced one potential set of applications of our technology concept from TRL 2 to TRL 3 and also identified a previously unknown set of applications and advanced it from TRL 1 to TRL 2. Overall, we have moved the original formulation of our concept forward from TRL 2 to TRL 3, and the expanded formulation of it presented in this document has been advanced from a combination of TRL 1 and early 1RL 2 to an overall late TRL 2. We have also identified the key areas necessary for both short-term and long-term advancement, and made recommendations for specific future work in the most promising directions. With future work on a 1-2 year timeframe to continue advancement to overall TRL 3, we will be well positioned to begin work on a specific space mission technology insertion path.
Biology↗
Perspective on Solar Energy Materials, Design and Discovery, Defects, Disorder, and Interfaces
The energy transition still faces a daunting math. Currently, more than three quarters of final energy consumption occurs in form of fuels vs less than one quarter in electricity. On the other hand, renewable energy additions come almost exclusively in the form of electricity (dominantly photovoltaics and wind). Thus, meeting the terawatt challenge requires enormous growth in renewables, sufficient to convert excess electricity into fuels, as well as the development of non-electricity based solar fuel technologies, for example via solar thermochemical routes. In this presentation, we will review successes and challenges in photovoltaic materials from silicon to CdTe to halide perovskites and potential emerging materials, with a particular perspective on the role of first principles calculations and predictions. Materials design and discovery is evolving to incorporate defects, disorder, and interfaces. These phenomena play also an important role in the thermochemical generation of hydrogen, allowing to utilize synergies between renewable electricity and fuels in computational research. At the same time, machine learning approaches are increasingly geared toward properties of non-ideal materials, for example defect formation, allowing the screening for more complex material behavior as needed for the respective applications.
fuels↗
Integrated membrane material design and system synthesis
In designing membrane systems, the synergy between membrane materials and the process design is often overlooked. In this paper, we present a mixed-integer nonlinear programming (MINLP) model for synthesizing membrane systems while simultaneously designing the respective membrane materials for multicomponent gas separation. The approach considers superstructure representations for systems with: (1) same, (2) potentially different, and (3) property-targeting membrane materials. In the first two systems, the selection of membrane material is a decision, while in the final type, membrane permeances are subject to optimization. Physics-based surrogate models are used to describe permeation in crossflow and countercurrent flow permeators. We show that, through a case study of biogas upgrading, our approach obtains high quality solutions. Furthermore, we use the proposed approach while considering permeance-based production cost to find the optimal membrane.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
AI for Materials Design and Discovery Using Atomistic Scale Information [Industrial and Governmental Activities]
The design and discovery of materials with desired functional properties is pivotal to the scientific mission of the United States Department of Energy (US-DOE) [1], which includes within its portfolio several important applications for the national economy and security. Importantly, these applications range from: renewable energy (e.g., solar cells, organic photovoltaics, and organic light-emitting diodes), energy storage (e.g., batteries and supercapacitors), and carbon capture and sequestration, to synthesis of manufacturing of new materials (e.g., drugs, or materials with desired conductivity, thermal stability, and catalytic activity), and nuclear energy (e.g., highly performant nuclear fuels and materials with improved nuclear shielding properties).
97 MATHEMATICS AND COMPUTING↗
Materials Design Directions for Solar Thermochemical Water Splitting
The sustainable, economical production of molecular hydrogen is a crucial component of a net zero-greenhouse-gas-emissions future. Solar thermochemical water splitting (STWS) offers a renewable route to hydrogen with the potential to help decarbonize several industries, including transportation, manufacturing, mining, metals processing, and electricity generation, as well as provide sustainable hydrogen as a chemical feedstock. STWS uses high temperatures generated from concentrated sunlight or other sustainable means for high-temperature heat to produce hydrogen and oxygen from steam. For example, in its simplest form of a two-step thermochemical cycle, a redox-active metal oxide is heated to ≈1700-2000 K, driving off molecular oxygen while producing oxygen vacancies in the material. The reduced metal oxide then cools (ideally with the extracted heat recuperated for re-use) and, in a separate step, comes into contact with steam, which reacts with oxygen vacancies to produce molecular hydrogen while recovering the original state of the metal oxide. Despite its promising use of the entire solar spectrum to split water thermochemically, the current estimated cost of hydrogen produced via STWS is ≈4-6× the U.S. Department of Energy (DOE) Hydrogen Shot target value of $1/kg. One contributing approach to bridging this cost gap is the design of new materials with improved thermodynamic properties to enable higher efficiencies. The state-of-the-art (SOA) redox-active metal oxide for STWS is ceria (CeO 2 ), due to its close to optimal, although too high, oxygen vacancy formation enthalpy and large configurational and electronic entropy of reduction. However, ceria requires high operating temperatures and its efficiency is insufficient. Therefore, efforts to increase the efficiency of STWS cycles have focused on further optimizing oxygen vacancy formation enthalpies and augmenting the reduction entropy via substitution or doping and materials discovery schemes. Examples of the latter include the perovskites BaCe 0.25 Mn 0.75 O 3 and (Ca,Ce)(Ti,Mn)O 3 . These efforts and others have revealed intuitive chemical principles for the efficient and systematic design of more effective materials, such as the strong correlation between the enthalpies of crystal bond dissociation and solid-state cation reduction with the enthalpy of oxygen vacancy formation, as well as configurational entropy augmentation via the coexistence of two or more redox-active cation sublattices. The purpose of this chapter is to prepare the reader with an up-to-date account of STWS redox-active materials, both the SOA and promising newcomers, as well as to provide chemically intuitive strategies for improving their cycle efficiencies through materials design – in conjunction with ongoing efforts in reactor engineering and gas separations – to reach the cost points for commercial viability. First, we will introduce the thermodynamics of STWS using a two-step, metal-oxide, thermochemical cycle with economics in mind. We also will compare the pros and cons of processes that do or do not involve phase changes. Second, we will describe the qualities that make ceria the SOA STWS redox-active material, as well as its limitations. Third, we will survey some of the most promising candidates to date in the search for materials to supplant ceria, emphasizing the post-ternary, metal-oxide-perovskite alloys. Lastly, we will enumerate and discuss the following materials design directions for STWS redox-active materials: crystal reduction potentials as a proxy for oxygen vacancy formation enthalpies, engineering the electronic and configurational entropy of reduction via f-shells and simultaneous redox, and vetting materials stability via temperature-dependent phase diagrams and melting-point prediction.
08 HYDROGEN↗
Energy dissipating structures - A structures- materials-design synthesis.
Ideal energy dissipating mechanisms to synthesize weight or mass index in terms of pertinent design indices, structural efficiency and material efficiency parameters
STRUCTURAL DESIGN↗