Search NASA⌕ Search

SEARCH · Search NASA

Results for “Chemistry and Materials”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integration Plan for NEAMS Structural Materials and Chemistry MSR Modeling

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.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Soft Materials Constructed Using Calix[4]pyrrole- and “Texas-Sized” Box-Based Anion Receptors

We present that soft materials have received considerable attention from supramolecular chemists and material scientists alike. This interest reflects the advantages provided by their soft, flexible nature and the convenience of the molecular self-assembly that underlies their preparation. Common soft supramolecular materials include polymeric gels, supramolecular polymers, nanoaggregates, and membranes. Polymeric gels are solidlike networks of cross-linked polymer chains. Supramolecular polymers contain repeat units connected through reversible non-covalent bonds. Nanoaggregates are formed as a result of hydrophobic interactions involving amphiphilic building blocks. Because of the presence of non-covalent interactions, supramolecular soft materials typically display stimuli-responsive or adaptive features. Various macrocyclic hosts, such as cyclodextrins, crown ethers, calixarenes, cucurbiturils, and pillararenes, and many classic non-covalent interactions have been harnessed to construct supramolecular soft materials. Only recently has anion binding been used as the underlying recognition motif. Anions are ubiquitous in the natural world. Their importance has inspired efforts to achieve good anion binding and to exploit anion recognition in a number of fields, including extraction, transport, sensing, and catalysis. Most of this effort has involved the use of stand-alone anion receptors. On the other hand, soft materials with anion recognition features could lead to new macromolecular systems of interest in the context of many application areas. In this Account, we summarize the latest efforts from our laboratory to prepare supramolecular soft materials, including polymeric gels, supramolecular polymers, and nanoaggregates, with bona fide anion recognition features. Two anion receptor systems, namely, calix[4]pyrroles (C4Ps) and a tetraimidazolium macrocycle known as the “Texas-sized” molecular box (TxSB), have been used for this purpose. To date, TxSB-based hydrogels have been utilized to capture anions from water and for coded information applications; C4P-based organic polymeric gels have been used to extract dianions from aqueous source phases and for the on-site detection of chloride anions. Polymers containing C4P and TxSB anion recognition subunits typically display responsive features and can be modified through application of appropriately chosen external stimuli. For instance, nanoaggregates may be formed as a result of the hydrophobic interactions of C4P- and TxSB-based amphiphiles. The resulting aggregates were found to mimic the structural evolution of organelles and could be used as effective anion and ion pair extractants. This Account summarizes progress to date while underscoring potential opportunities associated with combining anion recognition and soft materials chemistry. Finally, the hope is to stimulate further advances in broad areas, including polymer science, supramolecular chemistry, biology, materials research, and information storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

ML4Chem: Machine Learning for Chemistry and Materials v0.0.1

ML4Chem is a free, and open-source machine learning package for chemistry and materials sciences written in Python. It allows scientists to focus on applying the most commonly used machine learning models in chemistry and materials sciences with minimum effort.

de Jong, Wibe↗
DOE OSTI2022

Suppression of Superconductivity and Nematic Order in Fe 1–y Se 1–x S x (0 ≤ x ≤ 1; y ≤ 0.1) Crystals by Anion Height Disorder

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.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

High performance bulk thermoelectric materials and flexible transparent electrodes. Final Technical Report

This is the final report of the grant for the period from January 15, 2013 to November 14, 2018. During the 6 years, we have carried out a number of research projects and have published 97 peer-reviewed journal papers in Nature Nanotechnology, Nature Communications, Energy & Environmental Science, PNAS, NanoLetters, Nano Energy, Advanced Functional Materials, Advanced Electronic Materials, Chemistry of Materials, etc. Among them, 8 are highly cited and have generated a significant impact to the fields of thermoelectrics, flexible transparent electronics, water splitting for hydrogen generation, enhanced oil recovery, etc. More than 20 invention disclosures have been filed, which are being processed by the US patent and trade office.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Self-Driving Laboratories for Chemistry and Materials Science

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Foundation models for atomistic simulation of chemistry and materials

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.

Yuan, Eric C.-Y. [University of California, Berkel↗
DOE OSTI2024

Trust Not Verify? The Critical Need for Data Curation Standards in Materials Informatics

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.

Chemical structure↗
DOE OSTI2024

Developing and Running Quantum Algorithms for Chemistry and Materials (QAChMat) (Final Technical Report)

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.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations

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.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept

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.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Facilitating Ionic and Electronic Conduction in Radical Polymers through Controlled Assembly

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.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Role of Grain Boundary Structure and Chemistry in Materials Failure

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.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Integrated Fuel Cycle Materials and Chemistry Program (FY21 Plutonium Science Task Report)

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.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Multireference Methods for Chemistry and Materials Science: Automated Active Spaces, Efficient Dynamic Correlation, and Extended Systems

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Installation and commissioning of the ion source systems for the new spallation neutron source 2.5 MeV injector

The U.S. Spallation Neutron Source (SNS) is a state-of-the-art neutron scattering facility delivering the world’s most intense pulsed neutron beams to a wide array of instruments, which are used to conduct investigations in many fields of engineering, physics, chemistry, material science, and biology. Neutrons are produced by spallation of liquid Hg by the bombardment of short (∼1 µs), intense (∼35 A) pulses of protons delivered at 60 Hz by an accumulator ring which is fed by a high-intensity, 1 GeV, H− LINAC (linear accelerator). This facility has operated nearly continuously since 2006 but has recently undergone a 4-month maintenance period, which featured a complete replacement of the 2.5 MeV injector feeding the LINAC. The new injector was developed at ORNL in an off-line beam test facility and consists of an ion source, low energy beam transport, and a Radio Frequency Quadrupole (RFQ). This report first describes the installed configuration of the new injector detailing the ion source system. The first beam current, RFQ transmission, emittance, and energy measurements from the injector installed on the SNS are reported. These data not only show a significant performance improvement for our existing facility but will also make accessible the higher beam current requirements for future SNS upgrade projects: the proton power upgrade and second target station.

Welton, R. F.↗
DOE OSTI2020

Energy storage emerging: A perspective from the Joint Center for Energy Storage Research

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.

Trahey, Lynn↗