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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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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

High-Throughput Automated Exploration of Phase Growth Behaviors in Quasi-2D Formamidinium Metal Halide Perovskites

Quasi-2D metal halide perovskites (MHPs) are an emerging material platform for sustainable functional optoelectronics, but the uncontrollable, broad phase distribution remains a critical challenge for applications. Nevertheless, the basic principles for controlling phases in quasi-2D MHPs remain poorly understood, due to the rapid crystallization kinetics during the conventional thin-film fabrication process. In this work, a high-throughput automated synthesis-characterization-analysis workflow is implemented to accelerate material exploration in formamidinium (FA)-based quasi-2D MHP compositional space, revealing the early-stage phase growth behaviors fundamentally determining the phase distributions. Upon comprehensive exploration with varying synthesis conditions including 2D:3D composition ratios, antisolvent injection rates, and temperatures in an automated synthesis-characterization platform, it is observed that the prominent n = 2 2D phase restricts the growth kinetics of 3D-like phases—α-FAPbI 3 MHPs with spacer-coordinated surface—across the MHP compositions. Thermal annealing is a critical step for proper phase growth, although it can lead to the emergence of unwanted local PbI 2 crystallites. Additionally, fundamental insights into the precursor chemistry associated with spacer-solvent interaction determining the quasi-2D MHP morphologies and microstructures are demonstrated. The high-throughput study provides comprehensive insights into the fundamental principles in quasi-2D MHP phase control, enabling new control of the functionalities in complex materials systems for sustainable device applications.

2D perovskites↗
DOE OSTI2022

Observational process data analytics using causal inference

Voluminous process data are available with the paradigm shift toward smart manufacturing. However, most historical data are observational, containing noncausal correlations due to confounders and mediators. Estimating causal effects from observational data remains a bottleneck in leveraging them for active applications such as optimization and control. Further, this work aims to introduce a causal modeling framework for analyzing observational process data and extracting quantitative causal information. We demonstrate a real-world application in steel manufacturing where causal inference is used to analyze observational production data and improve the steelmaking process. Additionally, we propose a novel formulation for identifying critical process parameters from observational data, where causal inference is combined with variance-based methods to estimate corresponding risks of interventions to the manufacturing system. The proposed methods are compared with statistical ones to illustrate that causally interpreting statistical correlation leads to problematic results, while the provided workflow generates satisfactory strategies for process improvement.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Recent advancements in molecular Mass Spectrometry Imaging have sparked interest in integrating high spatial resolution methods with molecular mass-spectrometry-based chemical imaging. Fusion-based algorithms have proven effective in generating high spatial-resolution molecular mass spectra. However, a significant challenge stems from the differing physical mechanisms underlying image generation and data upsampling techniques, potentially leading to discrepancies in integrated information channels. Integrating physical constraints into data processing workflows is essential to tackle this issue. In this study, we propose an innovative approach that merges data from Fourier transform ion cyclotron resonance (FTICR), time-of-flight matrix-assisted laser desorption/ionization, and time-of-flight secondary ion mass spectrometry imaging techniques. By leveraging FT-ICR's unparalleled spectral resolution and ToF-SIMS's exceptional spatial resolution, we achieve submicron spatial resolution, enabling the observation of intact molecular species with remarkable spectral precision. Canonical correlation analysis is employed to incorporate physical constraints. Through sophisticated image processing and machine learning techniques, the results of this fusion hold significant promise for advancing our comprehension of complex systems and unveiling concealed molecular intricacies.

canonical correlation analysis↗
DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation

Molecular-level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real-time operation. Digital off-axis holography (DOAH) provides high-throughput, label-free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence offers a powerful route to address these limitations by uniting physics-based reconstruction with data-driven interpretation. In this Perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI-driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi-agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with true paramagnetic and diamagnetic behavior. This demonstration shows how AI-enabled reasoning can deliver real-time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next-generation interfacial science.

Ricchiuti, Giovanna↗
DOE OSTI2025

Targeted Chemical Looping Materials Discovery by an Inverse Design

Chemical looping with oxygen uncoupling (CLOU) materials is actively sought for combustion of carbonaceous materials to achieve complete conversion and capture of carbon dioxide. These materials may play a vital role in reducing atmospheric carbon via negative carbon output. However, there is no one‐size‐fits‐all approach as different operating conditions and feedstocks may require different CLOU materials. As a result, the exploration and discovery of high‐performance CLOU materials can be a slow process. To address this challenge, a high‐throughput inverse machine learning workflow that identifies optimum materials from perovskite oxides for a given set of targets is developed—temperature and Gibbs free energy of oxygen formation. The model is trained on high‐throughput density functional theory calculations of CLOU materials and inverts the materials design process using a genetic algorithm to produce realistic substituted SrFeO 3‐δ compositions as output. Using the inverse model, it is able to identify several interesting new families of CLOU materials: Sr 1‐ x A x Fe 1‐ y B y O 3‐δ (e.g., A = Ca or K; B = Mg, Bi, Mn, Ni, Co, Cu, or Zn). These materials have shown promising properties, and some of them even outperform the benchmark material in terms of oxygen release kinetics under relevant CLOU operating conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Redefining Design for Remanufacturing: A Practical Methodology for Prioritizing Remanufacturing Design Rules

Products are often discarded when they fail or no longer meet user needs. These outcomes are frequently shaped by early design decisions. While remanufacturing offers a sustainable alternative by restoring products to like‐new condition, its potential is often limited by designs that do not consider remanufacturing from the outset. This research addresses that challenge by introducing a structured Design for Remanufacturing (DfRem) methodology and a CAD‐integrated tool to support real‐time design decisions. The DfRem framework introduces a new primary design function focused on preserving product functionality across its life cycle. It is supported by a fault tree that identifies failure modes that limit remanufacturing potential and a hierarchy of design principles including Prevent, Minimize, Relocate, Restore, and others. Each principle is linked to actionable design rules that help engineers reduce the need for remanufacturing or improve its efficiency when necessary. To operationalize this framework, we developed CAD plugins for Autodesk Inventor and PTC Creo. These tools use a state machine model to present prioritized design rules based on selected failure modes and user input. By embedding DfRem logic directly into widely used CAD environments, the tool enables engineers to make sustainability‐informed decisions without disrupting existing workflows. Furthermore, this approach highlights the critical role of design in enabling circular and resource‐efficient product development, making remanufacturing a more practical and accessible strategy during the early stages of product design.

CAD↗
DOE OSTI2024

A Semi‐Automated, High‐Throughput Approach for the Synthesis and Identification of Highly Photo‐Cytotoxic Iridium Complexes

Abstract The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise‐and‐test cycles, with the aim of identifying potential agents for photodynamic therapy. We demonstrate the power of this approach by identifying highly active complexes that are well‐tolerated in the dark but display very low nM phototoxicity against cancer cells. To build a detailed structure–activity relationship for this class of compounds we have used density functional theory (DFT) calculations to determine some key electronic parameters and study correlations with the experimental data. Finally, we present an optimised semi‐automated synthesise‐and‐test protocol to obtain multiplex data within 72 hours.

Kench, Timothy↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2025

Machine‐Learning‐Driven Exploration of Surface Reconstructions of Reduced Rutile TiO 2

Abstract Titanium dioxide (TiO 2 ) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water‐gas shift (RWGS) reaction. Reduced TiO 2 surfaces undergo complex surface reconstructions that endow unique properties but are computationally challenging to describe. In this study, we utilize machine‐learning interatomic potentials (MLIPs) integrated with an active‐learning workflow to efficiently explore reduced rutile TiO 2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results by comparing experimental and theoretical high‐resolution transmission electron microscopy (HRTEM). Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO 2 , with potential implications for catalyst design.

Lee, Yonghyuk [Chemistry and Biochemistry Universi↗
DOE OSTI2024

A Semi‐Automated, High‐Throughput Approach for the Synthesis and Identification of Highly Photo‐Cytotoxic Iridium Complexes

Abstract The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise‐and‐test cycles, with the aim of identifying potential agents for photodynamic therapy. We demonstrate the power of this approach by identifying highly active complexes that are well‐tolerated in the dark but display very low nM phototoxicity against cancer cells. To build a detailed structure–activity relationship for this class of compounds we have used density functional theory (DFT) calculations to determine some key electronic parameters and study correlations with the experimental data. Finally, we present an optimised semi‐automated synthesise‐and‐test protocol to obtain multiplex data within 72 hours.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2025

Machine‐Learning‐Driven Exploration of Surface Reconstructions of Reduced Rutile TiO 2

Titanium dioxide (TiO 2 ) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water-gas shift (RWGS) reaction. Reduced TiO 2 surfaces undergo complex surface reconstructions that endow unique properties but are computationally challenging to describe. In this study, we utilize machine-learning interatomic potentials (MLIPs) integrated with an active-learning workflow to efficiently explore reduced rutile TiO 2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results by comparing experimental and theoretical high-resolution transmission electron microscopy (HRTEM). Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO 2 , with potential implications for catalyst design.

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

Combining ToF‐SIMS and Multivariate Analysis to Resolve Active Sites on Ni‐Based HER Catalysts

Unambiguous identification of active sites in heterogeneous catalysis remains a major challenge, particularly for materials with ultrathin, chemically mixed surface layers. Here, we demonstrate a generalizable approach that combines time-of-flight secondary ion mass spectrometry (ToF-SIMS) with multivariate statistical analysis (principal component analysis [PCA] and multivariate curve resolution [MCR]) to resolve catalytically relevant motifs at the nanoscale. Using Ni electrodes as a model system, PCA distinguished hydroxide-enriched domains from oxide- and metal-rich regions, while MCR decomposed depth profiles and 3D images into hydroxide, oxide, and metallic layers with nanometer resolution. A unique secondary-ion fragment, NiO 3 H 3 − (m/z 108.94), emerged as a marker of hydroxide-rich environments and correlated with hydrogen evolution reaction (HER) activity across a series of Ni electrodes. Complementary density functional theory (DFT) calculations revealed that Ni(OH) 2 clusters adjacent to metallic Ni offer the most favorable water dissociation energetics, establishing the structural origin of the marker. While illustrated here for Ni-based HER, this workflow provides a broadly applicable framework to isolate and rank near-surface patterns that govern catalytic activity, thereby extending ToF-SIMS from a qualitative probe to a predictive tool for active site identification.

HER active sites↗
DOE OSTI2022

Providing performance portable numerics for Intel GPUs

Summary With discrete Intel GPUs entering the high‐performance computing landscape, there is an urgent need for production‐ready software stacks for these platforms. In this article, we report how we enable the Ginkgo math library to execute on Intel GPUs by developing a kernel backed based on the DPC++ programming environment. We discuss conceptual differences between the CUDA and DPC++ programming models and describe workflows for simplified code conversion. We evaluate the performance of basic and advanced sparse linear algebra routines available in Ginkgo's DPC++ backend in the hardware‐specific performance bounds and compare against routines providing the same functionality that ship with Intel's oneMKL vendor library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Real‐time XFEL data analysis at SLAC and NERSC: A trial run of nascent exascale experimental data analysis

X‐ray scattering experiments using free electron lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID‐19 viral proteins). XFEL experiments are a challenge to computing in two ways: (i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; (ii) data‐collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min—sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS‐NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Completing data analysis within 10 min is a first for XFEL experiments and an important milestone if we are to keep up with data‐collection trends.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

SOMA: Observability, monitoring, and in situ analytics for exascale applications

With the rise of exascale systems and large, data-centric workflows, the need to observe and analyze high performance computing (HPC) applications during their execution is becoming increasingly important. HPC applications are typically not designed with online monitoring in mind, therefore, the observability challenge lies in being able to access and analyze interesting events with low overhead while seamlessly integrating such capabilities into existing and new applications. We explore how our service-based observation, monitoring, and analytics (SOMA) approach to collecting and aggregating both application-specific diagnostic data and performance data addresses these needs. Furthermore, we present our SOMA framework and demonstrate its viability with LULESH, a hydrodynamics proxy application. Then we focus on Astaroth, a multi-GPU library for stencil computations, highlighting the integration of the TAU and APEX performance tools and SOMA for application and performance data monitoring.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

17 O NMR Spectroscopy Reveals CO 2 Speciation and Dynamics in Hydroxide-Based Carbon Capture Materials

Carbon dioxide capture technologies are set to play a vital role in mitigating the current climate crisis. Solid-state 17 O NMR spectroscopy can provide key mechanistic insights that are crucial to effective sorbent development. In this work, we present the fundamental aspects and complexities for the study of hydroxide-based CO 2 capture systems by 17 O NMR. We perform static density functional theory (DFT) NMR calculations to assign peaks for general hydroxide CO 2 capture products, finding that 17 O NMR can readily distinguish bicarbonate, carbonate and water species. However, in application to CO 2 binding in two test case hydroxide-functionalised metal-organic frameworks (MOFs) – MFU-4l and KHCO 3 -cyclodextrin-MOF, we find that a dynamic treatment is necessary to obtain agreement between computational and experimental spectra. We therefore introduce a workflow that leverages machine-learning force fields to capture dynamics across multiple chemical exchange regimes, providing a significant improvement on static DFT predictions. In MFU-4l, we parameterise a two-component dynamic motion of the bicarbonate motif involving a rapid carbonyl seesaw motion and intermediate hydroxyl proton hopping. For KHCO 3 -CD-MOF, we combined experimental and modelling approaches to propose a new mixed carbonate-bicarbonate binding mechanism and thus, we open new avenues for the study and modelling of hydroxide-based CO 2 capture materials by 17 O NMR.

NMR spectroscopy↗