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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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Overview of NASA GRC Materials Development. New materials enabled by new chemistries offering unique properties and chemical processing techniques. Durability of materials in harsh environments requires understanding and modeling of chemical interaction of materials with the environment.
Abstract The recently introduced coupled cluster (CC) downfolding techniques for reducing the dimensionality of quantum many-body problems recast the CC formalism in the form of the renormalization procedure allowing, for the construction of effective (or downfolded) Hamiltonians in small-dimensionality sub-space, usually identified with the so-called active space, of the entire Hilbert space. The resulting downfolded Hamiltonians integrate out the external (out-of-active-space) Fermionic degrees of freedom from the internal (in-the-active-space) parameters of the wave function, which can be determined as components of the eigenvectors of the downfolded Hamiltonians in the active space. This paper will discuss the extension of non-Hermitian (associated with standard CC formulations) and Hermitian (associated with the unitary CC approaches) downfolding formulations to composite quantum systems commonly encountered in materials science and chemistry. The non-Hermitian formulation can provide a platform for developing local CC approaches, while the Hermitian one can serve as an ideal foundation for developing various quantum computing applications based on the limited quantum resources. We also discuss the algorithm for extracting the semi-analytical form of the inter-electron interactions in the active spaces.
As part of its broad objective to support advanced reactor development, the US Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has a strong interest in developing simulation capabilities to address chemistry and corrosion issues for molten salt reactors (MSRs). To that end, the Structural Materials & Chemistry (SMC) technical area within the NEAMS program is developing a set of codes and databases to describe the behavior of molten salts and their interactions with structural materials. This document reviews the current activities with respect to MSR-relevant codes and is an effort to improve their integration in simulating MSR systems. Interoperability between the codes being developed is critical for the success of the NEAMS-developed MSR simulation capability. This is greatly facilitated by having codes based on the MOOSE framework when possible, and by developing interfaces to MOOSE-based tools for those tools that are not based on that framework.
Connections between crystal chemistry and critical temperature T c have been in the focus of superconductivity, one of the most widely studied phenomena in physics, chemistry and materials science alike. In most Fe-based superconductors materials chemistry and physics conspire so that T c correlates with the average anion height above the Fe plane, i.e. with the geometry of the FeAs 4 or FeCh 4 (Ch = Te, Se, S) tetrahedron. Here, by synthesizing Fe 1-y Se 1-x S x (0 ≤ x ≤ 1, y ≤ 0.1) we find that in alloyed crystals T c is not correlated with the anion height as most other Fe superconductors. Instead, changes in T c (x) and tetragonal-to-orthorombic (nematic) transition T s (x) on cooling are correlated with disorder in Fe vibrations in direction orthogonal to Fe planes, along the crystallographic c-axis. The disorder stems from the random nature of S substitution, causing deformed Fe(Se,S) 4 tetrahedra with different Fe-Se and Fe-S bond distances. Our results provide evidence of T c and T s suppression by disorder in anion height. The connection to local crystal chemistry may be exploited in computational prediction of new superconducting materials with FeSe/S building blocks.
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, in this study, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.
The importance of data curation has been recognized in multiple areas of research; however, the discussion of this important issue is only beginning to emerge in materials science. In this Perspective, we highlight the benefits of using the standardized data curation protocols in materials science and discuss current gaps in accurate and reproducible data reporting using case studies drawn from high-impact materials science papers and well-known databases such as the Crystallography Open Database (COD) and the Cambridge Structural Database (CSD). We argue that both experimental and computational materials scientists need to embrace a culture of rigorous data curation as part of modern research data management. We propose a sample data curation pipeline for materials chemistry and illustrate its use by creating two new materials chemistry databases. Here, we hope that this perspective will serve to catalyze further discussion and promote the continuous development of rigorous data curation practices within the materials science research community. We posit that adherence to best practices of data curation will promote and enhance the reliability, reproducibility, and integrity of materials research and enable the development of reliable AI and machine learning models that critically depend on the use of quality data.
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The goal of the project is to design, develop, and execute new computational methods on practical quantum computing platforms to simulate hard problems in chemical and materials sciences. We exploited the two leading quantum computing platforms of trapped atomic ion and superconducting qubits, established in laboratories at Duke University and the University of Maryland, to discover new simulation and computational methods for the study of quantum chemistry and materials.
Friction consolidation (FC) is a solid phase processing approach where discrete material forms such as powders, chips, nuggets, etc. are densified via shear deformation. The precursors are placed in a billet container and brought in contact with a rotating tool that applying the desirable amount of normal force. Under the combined action of the rotation and normal pressure, the discrete precursor is consolidated through porosity reduction and shear deformation. FC is increasingly being studied as an attractive approach to manufacturing fully dense parts from powder forms owing to its ability to mix, alloy and consolidate difficult-to-process precursors in minimal number of process steps. Material consolidation and deformation in shear consolidation processes have been studied extensively previously for different material combinations previously. However, despite the extensive research in this area, understanding of the mechanistic processes in pore consolidation, deformation-induced mixing and material solubility during FC is still evolving. Material development using solid phase processing approaches such as FC is often performed based on research experience/education, which can be biased. Conventional analysis and simulation tools in this area tend to be successful only when material thermodynamic pathways and microstructural evolution sequences resulting from processing are clearly defined or known. They are not as effective for emerging advanced manufacturing technologies where material evolution pathways are not well established. The ability to predict optimal process parameters based on material chemistry and bulk properties is essential to accelerate materials design and processing, as are an understanding of the relevant structure-processing-property relationships. These structure-processing-property-performance relationships are at the core of materials science research. Microstructure characterization provides the link to these four core areas, often through visualizing material microstructure using imaging techniques. However, linking microstructure image data (i.e., micrographs) to variables of interest (e.g., processing parameters, material chemistry) in a reproducible, generalizable, and quantitative manner is a significant challenge. Typically, quantitatively linking image data to processing history relies on significant domain knowledge and manual or subject matter expert (SME)-heuristic based image analysis. Such an approach to image analysis has the potential to be biased, inefficient, and difficult to replicate.
This review proposes the concept of a “frugal twin,” similar to a digital twin, but for physical experiments. Frugal twins range from simple toy examples to low-cost surrogates of high-cost research systems. For example, a color-mixing self-driving laboratory (SDL) can serve as a low-cost version of a costly multi-step chemical discovery SDL. Frugal twins already provide hands-on experience for SDLs with low costs and low risks. They can also offer as test beds for software prototyping (e.g., optimization, data infrastructure), and a low barrier to entry for democratizing SDLs. However, there is room for improvement. The true value of frugal twins can be realized in three core areas. Firstly, hardware and software modularity; secondly, purpose-built design (human-inspired vs. hardware-centric vs. human-in-the-loop); and thirdly state-of-the-art (SOTA) software (e.g., multi-fidelity optimization). We also describe the ethical benefits and risks that come with the democratization of science through frugal twins. For future work, we suggest ideas for new frugal twins, SDL educational course outcomes, and a classification scheme for autonomy levels.
Parallel experiments have been performed in order to develop a comprehensive model for stress cracking (SCC) in structural materials. The central objective is to determine the relationship between the activity and selectivity of the microstructure of structural materials to their dissolution kinetics and experimentally measured SCC kinetics. Zinc was chosen as a prototype metal system. The SCC behavior of two oriented single-crystal disks of zinc in a chromic oxide/sodium sulfate solution (Palmerton solution) were determined. It was found that: (1) the dissolution rate is strongly (hkil)-dependent and proportional to the exposure time in the aggressive environment; and (2) a specific slip system is selectively active to dissolution under applied stress and this slip line controls crack initiation and propagation. As a precursor to potential microgrvity experiments, electrophoretic mobility measurements of zinc particles were obtained in solutions of sodium sulfate (0.0033 M) with concentrations of dissolved oxygen from 2 to 8 ppm. The equilibrium distribution of exposed oriented planes as well as their correlation will determine the particle mobility.
The major objectives, research performed, and significant results associated with this effort agree with the originally proposed work. That is, we have made significant advances in terms of both the experimental and computational thrusts of this effort, and these key results have allowed for us to have significant impact in the materials chemistry community. As is usual, this has led to even greater tangible product generation in the final year of the work relative to the first two years. Importantly, we have observed that the materials created under this effort have promising electronic and ionic conductivity properties along these lines, and we have developed the initial structure-property-performance relationships that offer future promise for these open-shell materials in advanced energy applications. One metric that represents this success is the number of publications that appear in notable journals regarding the work performed. Additionally, the team has been invited to present at many conferences of leading societies and at top academic institutions due to the work associated with this award. Finally, we anticipate that this will be of interest to multiple communities in the materials chemistry realm, and they will have broader impact into related technologies as well.
The objective of this project was to reveal how we can design materials with a tunable mechanical behavior through interface engineering. To do so we focused on understanding slip transmission across internal surfaces and how it relates to the strength and chemistry of the internal surface. In doing so we followed an interdisciplinary approach that employed theory, experiments and molecular dynamic (MD) simulations. Our guideline was that interfaces are characterized by their own mechanically induced interface parameter. New dislocation-grain boundary interaction mechanisms were revealed and the strengthening effects of non-traditional interfaces. We began studying simple grain boundaries in Fe systems, and then moved on to newly explored systems such as graphene-metal and nanolayer composites.
Task 2 of the IFCMCP was designed to utilize vibrational spectroscopy, microscopy, X-ray diffraction, and calorimetry to explore the spectroscopic, structural, and chemical properties of plutonium compounds relevant to the nuclear fuel cycle. An important precursor to this work is production of fresh, high-quality plutonium samples. Given the health hazards and material accountability associated with plutonium, sample production is not trivial and production experiments require extensive planning and coordination by research and support staff. Further, because alpha-emissions from plutonium produce time- and dose dependent radiolysis resulting in structural defects, particularly in the presence of water, freshly prepared plutonium samples must be carefully packaged and analyzed with urgency following their production. In FY21, SRNL and UND engaged in numerous research studies involving production and subsequent characterization of plutonium dioxide, plutonium fluoride, plutonium oxalate, and plutonium nitrate. Specific efforts involving each of these compounds are described in detail in the body of this report.
While multiconfigurational approaches have long been relegated to expert practitioners working on a case-by-case basis, recent developments have increasingly made these methods more routine and applicable to broader sets of systems. This article outlines the state-of-the-art in multiconfigurational approaches, with an emphasis on moving from delicate hand-selected pathways through configuration space toward more robust and efficient approaches to treating a host of challenging chemical systems accurately. First, we overview recent work in automated active-space selection, which has enabled increasingly large-scale applications of multireference methods to modeling vertical excitations and reactivity. Second, we highlight the increasingly efficient methods for recovering correlation energy beyond the active space, as headlined by extensions of pair-density functional theory and its role in accurate and efficient treatment of excited-state dynamics and its utilization to train machine-learned potentials. Finally, we highlight recent efforts to treat extended systems that until recently have lied beyond the traditional limits of active-space methods, giving center stage to product-form wave functions of the localized active space family of methods that allow for the computation of multiconfigurational band structures. These recent advancements point to a broader use of multireference approaches for high-impact chemical and materials science applications.
Energy storage is an integral part of modern society. A contemporary example is the lithium (Li)-ion battery, which enabled the launch of the personal electronics revolution in 1991 and the first commercial electric vehicles in 2010. Most recently, Li-ion batteries have expanded into the electricity grid to firm variable renewable generation, increasing the efficiency and effectiveness of transmission and distribution. Important applications continue to emerge including decarbonization of heavy-duty vehicles, rail, maritime shipping, and aviation and the growth of renewable electricity and storage on the grid. This perspective compares energy storage needs and priorities in 2010 with those now and those emerging over the next few decades. The diversity of demands for energy storage requires a diversity of purpose-built batteries designed to meet disparate applications. Advances in the frontier of battery research to achieve transformative performance spanning energy and power density, capacity, charge/discharge times, cost, lifetime, and safety are highlighted, along with strategic research refinements made by the Joint Center for Energy Storage Research (JCESR) and the broader community to accommodate the changing storage needs and priorities. Innovative experimental tools with higher spatial and temporal resolution, in situ and operando characterization, first-principles simulation, high throughput computation, machine learning, and artificial intelligence work collectively to reveal the origins of the electrochemical phenomena that enable new means of energy storage. This knowledge allows a constructionist approach to materials, chemistries, and architectures, where each atom or molecule plays a prescribed role in realizing batteries with unique performance profiles suitable for emergent demands.
Since its inception, photoelectrochemistry has sought to power the generation of fuels, particularly hydrogen, using energy from sunlight. Efficient and durable photoelectrodes, however, remain elusive. Here we review the current state of the art, focusing our discussion on advances in photoelectrodes made in the past decade. We open by briefly discussing fundamental photoelectrochemical concepts and implications for photoelectrode function. We next review a broad range of semiconductor photoelectrodes broken down by material class (oxides, nitrides, chalcogenides, and mature photovoltaic semiconductors), identifying intrinsic properties and discussing their influence on performance. We then identify innovative in situ and operando techniques to directly probe the photoelectrode|electrolyte interface, enabling direct assessment of structure–property relationships for catalytic surfaces in active reaction environments. We close by considering more complex photoelectrochemical fuel-forming reactions (carbon dioxide and nitrogen reduction, as well as alternative oxidation reactions), where product selectivity imposes additional criteria on electrochemical driving force and photoelectrode architecture. By contextualizing recent literature within a fundamental framework, we seek to provide direction for continued progress toward achieving efficient and stable fuel-forming photoelectrodes.