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At least 127 records · Page 7

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DOE OSTI2026

Automated Membership Inference Attacks: Discovering MIA Signal Computations using LLM Agents

Membership inference attacks (MIAs), which enable adversaries to determine whether specific data points were part of a model's training dataset, have emerged as an important framework to understand, assess, and quantify the potential information leakage associated with machine learning systems. Designing effective MIAs is a challenging task that usually requires extensive manual exploration of model behaviors to identify potential vulnerabilities. In this paper, we introduce AutoMIA -- a novel framework that leverages large language model (LLM) agents to automate the design and implementation of new MIA signal computations. By utilizing LLM agents, we can systematically explore a vast space of potential attack strategies, enabling the discovery of novel strategies. Our experiments demonstrate AutoMIA can successfully discover new MIAs that are specifically tailored to user-configured target model and dataset, resulting in improvements of up to 0.18 in absolute AUC over existing MIAs. This work provides the first demonstration that LLM agents can serve as an effective and scalable paradigm for designing and implementing MIAs with SOTA performance, opening up new avenues for future exploration.

Tran, Toan Viet [Emory University]↗

DOE OSTI2024

Tailored ultrasound propagation in microscale metamaterials via inertia design

The quasi-static properties of micro-architected (meta)materials have been extensively studied over the past decade, but their dynamic responses, especially in acoustic metamaterials with engineered wave propagation behavior, represent a new frontier. However, challenges in miniaturizing and characterizing acoustic metamaterials in high-frequency (megahertz) regimes have hindered progress toward experimentally implementing ultrasonic-wave control. Here, we present an inertia design framework based on positioning microspheres to tune responses of 3D microscale metamaterials. We demonstrate tunable quasi-static stiffness by up to 75% and dynamic longitudinal-wave velocities by up to 25% while maintaining identical material density. Using noncontact laser-based dynamic experiments of tunable elastodynamic properties and numerical demonstrations of spatio-temporal ultrasound wave propagation, we explore the tunable static and elastodynamic property relation. This design framework expands the quasi-static and dynamic metamaterial property space through simple geometric changes, enabling facile design and fabrication of metamaterials for applications in medical ultrasound and analog computing.

42 ENGINEERING↗

DOE OSTI2024

Automated path planning for functionally graded materials considering phase stability and solidification behavior: Application to the Mo-Nb-Ta-Ti system

Functionally graded materials have the potential to improve upon monolithic parts by locally tailoring compositions to surrounding environmental conditions. Difficulties arise when designing composition gradients as incompatible materials can result in detrimental phase formation and failure of the gradient joint. As many alloys are multi-component, designing a composition gradient free of detrimental phases is difficult due to the large composition space available to explore. A framework was developed that improves the path planning algorithm and surrogate models with adaptive sampling schemes specific to their problem definition. A cost function was created to minimize a property (such as cracking susceptibility) along a path. This framework was applied to the Mo-Nb-Ta-Ti system as a case study to showcase the efficiency in building the surrogate models and in iterating different optimal compositionally graded paths.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Fabrication of metal-organic framework thin films for luminescent sensing applications

Metal−organic frameworks (MOFs) have emerged as an exciting material class due to their nearly infinite design space: an inexhaustible combination of metal centers, organic linkers, and reaction conditions may be leveraged to obtained desired optical, physical, and chemical properties. This rich synthetic diversity enables properties such as pore dimensions and functionality to be rationally tailored for highly selective and sensitive detection of target analytes. Luminescent MOF-based sensors in particular offer advantages including low cost, portability, and ease-of-use. Often, performance is maximized by utilizing the luminescent MOF in thin film form, for example by integrating the film onto an optical fiber, yet the synthesis of high-quality MOF thin films often requires tedious, expensive, and/or slow approaches. Here, we demonstrate a rapid, simple strategy for growing copper MOFs using metal oxide templates; the concept is first demonstrated with a well-studied system, copper-1,3,5-benznetricarboxylate (Cu-BTC). Variables such as the choice of solvent, pH, and the choice of the copper salt anion all govern thin film quality. We then extend this method to synthesize a solvent and metal-responsive copper MOF that is particularly effective at detecting aluminum, an economically critical metal, at trace concentrations using a fully portable luminescence spectrometer. Taken together, this work demonstrates a convenient, versatile, and sustainable method for developing high quality MOF thin film-based optical sensors.

Crawford, Scott↗

NASA NTRSDate not supplied

Digitally-Engineered Impact Resistant Aerogel Composites for MMOD Protection (DIRAC-MP)

This project implemented a digital-engineering approach to optimize the impact absorption performance of polymer aerogels and aerogel-based composites for Micrometeoroids and Orbital Debris (MMOD) containment. We developed a curated materials database and a machine-learning framework to derive composition-response relationships, enabling predictive design and targeted material selection. In support of experimental validation, a split Hopkinson pressure bar (SHPB) test rig, specifically adapted for low-density aerogel materials, was designed and built in-house. This project accelerates the development of new aerogel formulations, producing candidate materials tailored for enhanced impact-absorption behavior.

Sadeq Malakooti↗

DOE OSTI2022

Pore space partition of metal-organic frameworks for gas storage and separation

Pore space partition (PSP) concept is a synthetic design concept and can also serve as a structure analysis method useful for next-step synthetic planning and execution. PSP provides an integrated chemistry-topology-focused tool to design new materials platforms. While PSP is no less effective for making large-pore materials, the growing importance of small-molecule gas storage and separation for green-energy applications provides impetus for developing small-pore materials for which the PSP strategy is uniquely suited. Currently, the best embodiment of the PSP concept is the partitioned-acs (pacs) platform in which both fine or coarse adjustments to the building blocks have sparked a transformation of a prototype framework into a huge and continuously expanding family of chemically robust materials with controllable pore metrics and functionalities suitable for tailored applications. The pacs compositional diversity results from the platform’s intrinsic multi-module nature, geometric flexibility and tolerance towards individual module variations, and mutual structure-directing effects among various modules, all of which combine to enable the molecular-level uniform co-assemblies of chemical components rarely seen together elsewhere. Here, in this contribution, we present an overview of different pore space engineering methods and how different MOF materials have contributed to important advances in chemical stability, industrial gas storage and gas separation. In particular, we will focus on synthetic assembly of the pacs system, highlighting the differences of pacs materials from other MOF platforms and advantages of pacs materials in enhancing various MOF properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Li-ion battery design through microstructural optimization using generative AI

Lithium-ion batteries are used across various applications, necessitating tailored cell designs to enhance performance. Optimizing electrode manufacturing parameters is a key route to achieving this, as these parameters directly influence the microstructure and performance of the cells. However, linking process parameters to performance is complex, and experimental or modeling campaigns are often slow and expensive. This study introduces a fast computational optimization framework for electrode manufacturing parameters. A generative model, trained on a small dataset of microstructural images associated with different manufacturing parameters, efficiently generates representative microstructures for new parameters. This model is integrated into a Bayesian optimization loop that includes microstructure generation, characterization, and simulation, aiming to find optimal manufacturing parameters for a particular application. Significant improvement in the energy density of a 4680 cell is achieved through bespoke cell design, highlighting the importance of cell-scale normalization. The framework’s modularity allows its application to various advanced materials manufacturing scenarios.

batteries↗

DOE OSTI2025

Weakly Solvating Electrolyte to Enable Lithium- and Manganese-Rich Cathode-Based Li-Ion Batteries

Traditional ethylene carbonate (EC)-based electrolytes exhibit strong solvation power at the surface of the layered transition metal oxide cathodes, which accelerates transition metal dissolution. The subsequent migration and deposition of dissolved transition metal species on the anode surface lead to significant capacity fading. To overcome this difficulty, we report a weakly solvating, all-fluorinated electrolyte designed to mitigate transition metal dissolution. For the first time, the role of electrolyte solvation in suppressing transition metal dissolution is systematically investigated. The tailored electrolyte significantly reduces transition metal dissolution and enhances the electrochemical performance of Li- and Mn-rich (LMR) cathode/graphite cells. This solvation-modulating strategy offers a broadly applicable framework for stabilizing interphases in other earth-abundant cathode chemistries, which similarly demand kinetic protection against interfacial degradation.

25 ENERGY STORAGE↗

DOE OSTI2026

A bioinspired approach for adaptive solid-solid phase change material coatings with optimized surface features for passive thermal regulation

The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that surface texture, rather than the intrinsic phase transition of the SS PCM, dominates optical control. Relative to a flat SS PCM coating, optimized isotropic random roughness and broader range features yielded the highest passive heating power increase of about 144 % and 319 % respectively suitable for cold climates. Saharan ant-inspired features enhanced passive cooling for hot climates, achieving a 21.8 % improvement. For moderate climates, Butterfly-wing-inspired surface features provided a balanced enhancement of 19 % for heating and 7 % for cooling. Across all cases, the optimized surface features reduced combined heating and cooling energy demand more effectively than the baseline coating, while preserving material thickness. These findings demonstrate that climate-adaptive, optimized bio-inspired surface features can unlock the full potential of SS PCM coatings, providing a versatile pathway to significant energy savings in buildings and other applications. The methodology establishes a framework for designing next-generation adaptive envelopes that leverage natural photonic principles for high-impact, low-cost thermal regulation.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Estimating Return on Investment for Energy Technical Assistance Programs

The U.S. Department of Energy's Office of State and Community Energy Programs engaged the National Laboratory of the Rockies to assess the return on investment (ROI) of technical assistance (TA) programs that support state, local, and Tribal energy planning. Although TA delivers value through capacity building, stakeholder engagement, and knowledge transfer, these benefits are often intangible and challenging to monetize. This study reviews existing ROI frameworks and synthesizes the most relevant elements into a hybrid approach tailored to energy TA programs. The proposed framework integrates monetary and non-monetary outcomes through early logic model development, baseline data collection, and the use of proxies for intangible benefits. As a case study, this paper applies this approach to the Communities Local Energy Action Program (Communities LEAP), demonstrating how ROI can inform program design, data strategy, and performance assessment. Findings underscore that ROI should be applied selectively and planned from the outset to ensure data alignment and attribution accuracy. The framework offers TA practitioners a structured approach that can be leveraged for future programs to evaluate and communicate the multifaceted value of TA investments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2003

Twinning, Epitaxy and Domain Switching in Ferroelastic Inclusion Compounds

Our research is in the area of solid-state organic chemistry, which lies at the interface between physical organic chemistry and materials science. We use crystalline solids as models to probe fundamental issues about physical processes, molecular interactions and chemical reactions that are important for fabrication, stabilization and application of technological materials. Much of our most recent work has focused on the phenomena of ferroelastic and ferroelectric domain switching, in which application of an external force or electric field to a crystal causes the molecules inside the crystal to reorient, in tandem, to a new orientational state. To better understand and control the domain switching process, we have designed and synthesized over twenty closely related, ferroelastic organic crystals. Our approach has been to use crystalline inclusion compounds, in which one molecule (the guest) is trapped within the crystalline framework of a second molecule (the host). By keeping the host constant and varying the proportions and kinds of guests, it has been possible to tailor these materials so that domain switching is rapid and reversible (which is desirable for high technology applications). Inclusion compounds therefore serve as powerful systems for understanding the specific molecular mechanisms that control domain switching.

Hollingsworth, Mark D.↗

DOE OSTI2021

Optimal Design of Sustainable Ammonia-Based Food–Energy–Water Systems with Nitrogen Management

As the basis for virtually any form of nitrogen fertilizers, ammonia plays a vital role in agriculture; in addition, there has been an increased interest in its use as a carbon-free energy carrier. However, ammonia is also associated with two major environmental concerns: CO 2 emissions from the conventional production process and nitrogen pollution from the excessive use of ammonia-based fertilizers. To mitigate these environmental impacts, we develop an optimization framework for the design of a sustainable ammonia-based agricultural system that synergistically integrates the production of ammonia from renewable resources and effective measures for nitrogen management. The proposed model captures the effect of intermittency by incorporating both design and detailed operational decisions. Here, by applying a multiscale time representation that reduces the problem size and a tailored surrogate model that accurately approximates model nonlinearity, we are able to achieve optimal solutions within reasonable computation times. A computational case study is conducted using real-world data from a local farm in Morris, Minnesota, and the results indicate the trade-off between cost and nitrogen loss. Importantly, we show that practicing effective nitrogen management can significantly reduce the nitrogen loss with only a small increase in net present cost.

10 SYNTHETIC FUELS↗

DOE OSTI2026

Er Al :Al 2 ⁢O 3 for telecom-band photonics: Electronic structure and optical properties

Er-doped Al 2 ⁢O 3 is a promising host for telecom-band integrated photonics. Here, in this study, we combine ab initio calculations with a symmetry-resolved analysis to elucidate substitutional Er on the Al site (Er Al ) in 𝛼−Al 2 ⁢O 3 . First-principles relaxations confirm the structural stability of Er Al . We then use the local trigonal crystal-field symmetry to classify the Er-derived impurity levels by irreducible representations and to derive polarization-resolved electric-dipole selection rules, explicitly identifying the symmetry-allowed 𝑓−𝑑 hybridization channels. Kubo-Greenwood absorption spectra computed from Kohn-Sham states quantitatively corroborate these symmetry predictions. Furthermore, we connect the calculated intra-4⁢𝑓 line strengths to Judd-Ofelt theory, clarifying the role of 4⁢𝑓−5⁢𝑑 admixture in enabling optical activity. Notably, we predict a characteristic absorption near 1.47 µ⁢m (telecom band), relevant for on-chip amplification and emission. To our knowledge, a symmetry-resolved first-principles treatment of Er:Al 2 ⁢O 3 with an explicit Judd-Ofelt interpretation has not been reported, providing a transferable framework for tailoring rare-earth dopants in wide-band-gap oxides for integrated photonics. Our results for the optical spectra are in good agreement with experimental data. The resulting symmetry-based selection rules translate directly to polarization-dependent coupling in Al 2 ⁢O 3 integrated photonic waveguides and resonators, enabling device-level design of TE/TM-mode interaction with Er emitters in the 1.5-µ⁢m telecom band.

Er-doped Al2O3↗

DOE OSTI2025

Autonomous Synthesis of Metastable Materials Using a Modular Mixed-Flow Reactor

Understanding and controlling atomic-level processes at solid-liquid interfaces is key to advancing technologies in energy storage, carbon capture, critical element recovery, and materials synthesis. Many of these processes are dominated by the formation of short-lived intermediate precipitates that determine the final properties of synthesized materials. However, studying these intermediates is challenging due to their sensitivity and the reliance on trial-and-error methods. To address this, we developed an automated variable-volume mixed-flow reactor (MFR) to optimize metastable material synthesis and investigate rapid kinetic processes. This state-of-the-art MFR system, paired with an automated modeling framework, enables efficient synthesis and real-time analysis of transient phases. Benchmarking with advanced capabilities, such as wide-/small-angle X-ray scattering, allows us to resolve fast nucleation and growth dynamics that were previously inaccessible. By combining automation, ML-guided optimization, and tailored kinetic modeling, this approach provides a robust platform for improving material design and achieving precise control over solid-liquid reactions.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Multi-faceted framework for extrapolating early age flexural strength to facilitate rapid lifting/handling of high-volume fly ash precast members

Maintaining adequate early-age structural performance for precast concrete components has grown in importance as more sustainable mix designs become more widespread. Achieving high-early flexural strength is particularly crucial to facilitate rapid removal of hardened concrete components from formwork, often within 24 h after fresh concrete placement. Limited research has assessed the effectiveness of traditional design methods in correlating flexural strength with compressive strength for next-generation mix designs, or demonstrated extrapolation of such material performance to larger-scale structural tests. This paper presents a multi-faceted framework to reassess early-age flexural strength for concretes made with relatively high proportions of fly ash from both fresh and harvested sources. Here, the framework provides several pathways, from which the user can select based upon available resources and the specific application, to improve accuracy of early-age cracking moment calculations. Furthermore, the scope includes evaluation of strength performance under curing conditions emulative of those in a precast facility, recommending modulus of rupture equations which are more performance-driven than current design provisions, and experimental tests on prefabricated concrete beams to validate the proposed methodologies. Correlations of early-age strength with both concrete age and maturity measurements compare the effectiveness of utilizing in-situ data to further enhance the prediction methods. Ultimately, the proposed framework helped reduce errors when calculating cracking moment capacity at early ages by tailoring calculations to reflect mix-dependent behavior. Furthermore, most estimates of cracking moment were within 25 % of their corresponding experimental test results, thus promoting confidence for using these strategies with high-volume fly ash precast structures.

42 ENGINEERING↗

NASA NTRS2015

Aerodynamic Shape Optimization Benchmarks with Error Control and Automatic Parameterization

Results are presented for four optimization benchmark problems posed by the AIAA Aerodynamic Design Optimization Discussion Group. The benchmarks are intended to exercise optimization frameworks on representative airfoil and wing design problems. All problems involve drag minimization subject to geometric and aerodynamic constraints. Our design approach involves two forms of adaptation. First, the shape parameterization is gradually and automatically enriched from an initially coarse search space. Second, adjoint solutions are used to drive adaptive mesh refinement to control discretization error. The error threshold is tailored so that the nest meshes, with the greatest accuracy, are used only when nearing the optimum. On the inviscid airfoil design problem, while reducing the drag by a factor of 10, we show how the combination of progressive parameterization and tiered discretization error control can dramatically accelerate the optimization. On the viscous airfoil design problem, we use inviscid analysis-driven optimization to reduce the total drag by a factor of two. Next, we improve the span efficiency factor of a wing by performing twist optimization. Finally, we optimize the Common Research Model wing, managing to hold drag roughly fixed, while targeting an initially-violated pitching moment constraint. Our approach aims to introduce greater complexity and accuracy only when necessary to improve the design, and also support a greater degree of automation.

Anderson, George R.↗

DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗

DOE OSTI2024

Self–Healing and Stretchable Molecular Ferroelectrics with High Expandability

The interplay between crystal ordering and stretchability is frequently encountered in contemporary materials science, particularly in the case of ferroelectrics. The inherent dilemma arises when these materials need to withstand repetitive mechanical deformations or stretching without sacrificing their crystal integrity, all while retaining their remarkable ferroelectric properties and even exhibiting self-healing capabilities. This complexity further presents a significant challenge in the design and engineering of mechanically rigid molecular ferroelectric crystals, particularly for applications where both precise crystalline structure and mechanical adaptability are crucial. In this study, the humidity-controlled expansion and contraction, dissolution, and recrystallization of a self-assembled molecular ferroelectric-in-hydrogel framework are reported. Self-healing ferroelectric-in-hydrogel networks exhibit a recyclable humidity-tailored ionic conductivity from 2.86 × 10 –6 to 1.36 × 10 –5 S cm –1 , facilitating the stretchable piezoelectric sensing. Additionally, the dynamic bond reforming interactions are observed, leading to the tailoring of Young's modulus from 452 to 170 MPa, maintaining ferroelectricity under a strain of 20% with a piezoelectric coefficient of 15.7 pC N –1 . Upon lattice contraction, the molecular contacts undergo reforming, leading to the restoration of stretchable ferroelectrics/piezoelectrics and paving the way for stretchable bioelectronics for full-body motion monitoring. The capabilities highlighted here open avenues for stretchable and self-healing ferroelectric-in-hydrogel bioelectronic technologies.

36 MATERIALS SCIENCE↗