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Results for “Data synthesis”

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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data‐Efficient Generation of Synthetic Microstructures of Polymer‐Bonded Energetic Material With Fine‐Tuned Stable Diffusion

Among current deep learning approaches for synthetic image generation, diffusion-based models stand out in terms of algorithmic stability and ability to retain high-fidelity image features with detailed resolution. Here, in this work, we employ Dreambooth, a method for fine-tuning Stable Diffusion, on X-ray CT images of microstructure of the polymer-bonded form (PBX) of a commonly used high explosive, Pentaerythritol tetranitrate (PETN), which yields generative models for creating synthetic PBX images. The models developed here represent five classes (or ‘lots’) of microstructures and demonstrate successful generation of images of each class with high fidelity, as verified by computed classification accuracy of ∼ 94% or higher. Data augmentation afforded by such image synthesis can be used to more reliably decipher underlying statistics, build processing-structure correlations, recognize off-normal structural anomalies, and identify age-related changes. Ideas related to converting image data into appropriate density mapping and performing mesoscale simulation or surrogate modeling of detonation are also discussed.

Dreambooth↗
DOE OSTI2022

Technoeconomic Model Suggests Scaling-Up Perovskite Quantum Dots for Optoelectronics Warrants Improved Synthesis Yield, Solvent Recycling, and Automation

A comprehensive technoeconomic cost-analysis of perovskite quantum dot optoelectronics is reported. Using economies-of-scale considerations based on price data from prominent materials suppliers, higher QD synthesis yield, solvent recycling, and synthesis automation are shown to be critical for market-entry of this technology. Combinedly, this three-pronged approach drives quantum dot film fabrication costs down from >$50/m 2 to ~$2-3/m 2 .

14 SOLAR ENERGY↗
DOE OSTI2022

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis–nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Validation of non-negative matrix factorization for rapid assessment of large sets of atomic pair distribution function data

The use of the non-negative matrix factorization (NMF) technique is validated for automatically extracting physically relevant components from atomic pair distribution function (PDF) data from time-series data such as in situ experiments. The use of two matrix-factorization techniques, principal component analysis and NMF, on PDF data is compared in the context of a chemical synthesis reaction taking place in a synchrotron beam, applying the approach to synthetic data where the correct composition is known and on measured PDFs from previously published experimental data. The NMF approach yields mathematical components that are very close to the PDFs of the chemical components of the system and a time evolution of the weights that closely follows the ground truth. Lastly, it is discussed how this would appear in a streaming context if the analysis were being carried out at the beamline as the experiment progressed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine Learning Approaches for Nuclear Material Accounting Data from Irradiation and Reprocessing

We are currently exploring data analysis methods for their ability to strengthen the synthesis and evaluation of information generated within domestic and international safeguard regimes. Safeguard data typically includes rich heterogenous datasets amenable to advanced data analytics methods, such as machine learning. We have converted transactional data containing both numerical and categorical attributes from a domestic nuclear material control and accountability (NMC&A) system into a low-dimensional numerical structure via linear principal component analysis. This data representation allows for global structure discovery via cluster analysis, which can characterize the typical behavior of each of the primary types of transaction events. Furthermore, the structure of the top principal components captures the “typical behavior” of the data and is thus amenable to anomaly detection through statistical hypothesis testing. We explored this capability by generating erroneous permutations of the data and computing the Q-residual quantity increase associated with the information loss when these data are projected into the low-dimensional principal component analysis space representative of typical transactions.Future work will focus on identifying data transformations (e.g., graph networks) that more closely align with the inherent structure of these transactions to explain more salient information and disambiguate the underlying nuclear process—in this case, irradiation and reprocessing—from artifacts of NMC&A system transactional record keeping data.

Drescher, Adam↗
DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗
DOE OSTI2022

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomy via integrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed the challenges and opportunities in an endeavor to develop the next-generation AEP for ultimately realizing an autonomous or self-driving laboratory. In conclusion, this review will provide insights for researchers aiming to learn the frontier of ML in materials science and deploy AEP in their labs for accelerating material development.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-Throughput Ion Irradiation and Microstructural Characterization of Multi-Principal-Element Alloys

Multi-principal-element alloys (MPEAs) have garnered interest in the nuclear community for their apparent resistance to microstructural damage under irradiation. While both experimentation and simulation have demonstrated reduced hardening and void swelling in MPEAs, the relationship between composition and irradiation response is still poorly understood since a rich experimental database for MPEAs does not yet exist. In this work, to accelerate the generation of experimental irradiation-response data for MPEAs, high-throughput synthesis, irradiation, and characterization techniques have been employed. Specifically, additive manufacturing has been used to produce arrays of various MPEAs (1-cm2 coupons), which have been subsequently irradiated using 4-MeV Ni2+ ions to a peak damage of 200 dpa at 500 °C. Hardening and void swelling have been assessed using nanoindentation and trenching via plasma focused ion beam (PFIB), respectively. Trends in the experimental data relating hardening and void swelling behavior to composition have been analyzed using different machine learning approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy

Abstract Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous ‘blue carbon’ studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon‐storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R‐shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision‐making.

Holmquist, James R.↗
DOE OSTI2022

Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Toward autonomous design and synthesis of novel inorganic materials

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve “closed-loop” materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Lastly, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Understanding the Fluorination of Disordered Rocksalt Cathodes through Rational Exploration of Synthesis Pathways

Here, we have designed and tested several synthesis routes targeting a highly fluorinated disordered rocksalt (DRX) cathode, Li 1.2 Mn 0.4 Ti 0.4 O 1.6 F 0.4 , with each route rationalized by thermochemical analysis. Precursor combinations were screened to raise the F chemical potential and avoid the formation of LiF, which inhibits fluorination of the targeted DRX phase. MnF 2 was used as a reactive source of F, and Li 6 MnO 4 , LiMnO 2 , and Li 2 Mn 0.33 Ti 0.66 O 3 were tested as alternative Li sources. Each synthesis procedure was monitored using a multi-modal suite of characterization techniques including X-ray diffraction, nuclear magnetic resonance, thermogravimetric analysis, and differential scanning calorimetry. From the resulting data, we advance the understanding of oxyfluoride synthesis by outlining the key factors limiting F solubility. At low temperatures, MnF 2 consistently reacts with the Li source to form LiF as an intermediate phase, thereby trapping F in strong Li-F bonds. LiF can react with Li 2 TiO 3 to form a highly lithiated and fluorinated DRX (Li 3 TiO 3 F); however, MnO is not easily incorporated into this DRX phase. Although higher temperatures typically increase solubility, the volatility of LiF above its melting point (848 °C) inhibits fluorination of the DRX phase. Based on these findings, metastable synthesis techniques are suggested for future work on DRX fluorination.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learned synthesizability predictions aided by density functional theory

Abstract A grand challenge of materials science is predicting synthesis pathways for novel compounds. Data-driven approaches have made significant progress in predicting a compound’s synthesizability; however, some recent attempts ignore phase stability information. Here, we combine thermodynamic stability calculated using density functional theory with composition-based features to train a machine learning model that predicts a material’s synthesizability. Our model predicts the synthesizability of ternary 1:1:1 compositions in the half-Heusler structure, achieving a cross-validated precision of 0.82 and recall of 0.82. Our model shows improvement in predicting non-half-Heuslers compared to a previous study’s model, and identifies 121 synthesizable candidates out of 4141 unreported ternary compositions. More notably, 39 stable compositions are predicted unsynthesizable while 62 unstable compositions are predicted synthesizable; these findings otherwise cannot be made using density functional theory stability alone. This study presents a new approach for accurately predicting synthesizability, and identifies new half-Heuslers for experimental synthesis.

Lee, Andrew (ORCID:0000000153014295)↗
DOE OSTI2024

Novel insights into the role of translesion synthesis polymerase in DNA incorporation and bypass of 5-fluorouracil in colorectal cancer

Abstract 5-Fluorouracil (5-FU) is the first-line chemotherapeutic agent in colorectal cancer, and resistance to 5-FU easily emerges. One of the mechanisms of drug action and resistance of 5-FU is through DNA incorporation. Our quantitative reverse-transcription PCR data showed that one of the translesion synthesis (TLS) DNA polymerases, DNA polymerase η (polη), was upregulated within 72 h upon 5-FU administration at 1 and 10 μM, indicating that polη is one of the first responding polymerases, and the only TLS polymerase, upon the 5-FU treatment to incorporate 5-FU into DNA. Our kinetic studies revealed that 5-fluoro-2′-deoxyuridine triphosphate (5FdUTP) was incorporated across dA 41 and 28 times more efficiently than across dG and across inosine, respectively, by polη indicating that the mutagenicity of 5-FU incorporation is higher in the presence of inosine and that DNA lesions could lead to more mutagenic incorporation of 5-FU. Our polη crystal structures complexed with DNA and 5FdUTP revealed that dA:5FdUTP base pair is like dA:dTTP in the active site of polη, while 5FdUTP adopted 4-enol tautomer in the base pairs with dG and HX increasing the insertion efficiency compared to dG:dTTP for the incorrect insertions. These studies confirm that polη engages in the DNA incorporation and bypass of 5-FU.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Assessing the Effectiveness of Large-Scale Environmental Restoration: Challenges and Opportunities

A recent National Academies consensus report addresses monitoring and assessment of cumulative effects of large-scale and multiple restoration projects within the context of long-term environmental change. Fines and penalties from the Deepwater Horizon oil spill in the Gulf of Mexico (GoM) have supported hundreds of restoration projects at spatial scales not often possible in the past. Here, in this paper, the report committee members and staff provide personal reflections from our time working on the study. We found that gaps in data collection, issues with data accessibility, and a lack of synthesis and analysis are hindering the ability to answer a basic question: What are the impacts of these many restoration efforts on improving ecosystem health and productivity in the GoM at the regional and Gulf wide scale? Restoration efforts are occurring in environments where many trends are changing and exhibiting higher variability than in the past, suggesting that previously successful restoration practices may no longer be adequate to compensate for the effects of environmental changes and variability. Our proposed approach to these challenges includes employing emerging monitoring technologies; using conceptual models; devising an adaptive management framework; rethinking restoration outcome goals; assessing cumulative effects; and undertaking rigorous synthesis and analysis of existing information on long-term environmental trends and restoration efforts. Restoration scientists and practitioners working in the GoM have an unprecedented opportunity to demonstrate large-scale environmental recovery if advances in monitoring, synthesis, assessment, and action are taken quickly. We are cautiously optimistic that, with mid-course adjustments, continued progress toward large-scale environmental recovery is possible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Photodriven Ammonia Synthesis from Manganese Nitrides: Photophysics and Mechanistic Investigations

Ammonia synthesis from N,N,O,O-supported manganese(V) nitrides and 9,10-dihydroacridine using proton-coupled electron transfer and visible light irradiation in the absence of precious metal photocatalysts is described. While the reactivity of the nitride correlated with increased absorption of blue light, excited-state lifetimes determined by transient absorption were on the order of picoseconds. Furthermore, this eliminated excited-state manganese nitrides as responsible for bimolecular N–H bond formation. Spectroscopic measurements on the hydrogen source, dihydroacridine, demonstrated that photooxidation of 9,10-dihydroacridine was necessary for productive ammonia synthesis. Transient absorption and pulse radiolysis data for dihydroacridine provided evidence for the presence of intermediates with weak E–H bonds, including the dihydroacridinium radical cation and both isomers of the monohydroacridine radical, but notably these intermediates were unreactive toward hydrogen atom transfer and net N–H bond formation. Additional optimization of the reaction conditions using higher photon flux resulted in higher rates of the ammonia production from the manganese(V) nitrides due to increased activation of the dihydroacridine.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗