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DOE OSTI2025

Data-Driven Compositional Optimization in Misspecified Regimes

With a manifold growth in the scale and intricacy of systems, the challenges of parametric misspecification become pronounced. These concerns are further exacerbated in compositional settings, which emerge in problems complicated by modeling risk and robustness. In “Data-Driven Compositional Optimization in Misspecified Regimes,” the authors consider the resolution of compositional stochastic optimization problems, plagued by parametric misspecification. In considering settings where such misspecification may be resolved via a parallel learning process, the authors develop schemes that can contend with diverse forms of risk, dynamics, and nonconvexity. They provide asymptotic and rate guarantees for unaccelerated and accelerated schemes for convex, strongly convex, and nonconvex problems in a two-level regime with extensions to the multilevel setting. Surprisingly, the nonasymptotic rate guarantees show no degradation from the rate statements obtained in a correctly specified regime and the schemes achieve optimal (or near-optimal) sample complexities for general T-level strongly convex and nonconvex compositional problems.

Business & Economics↗

DOE OSTI2025

Coupling Amorphization and Compositional Optimization of Ternary Metal Phosphides toward High-Performance Electrocatalytic Hydrogen Production

Amorphous materials, with abundant active sites and unique electronic configurations, have the potential to outperform their crystalline counterparts in high-performance catalysis for clean energy. However, their synthesis and compositional optimization remain underexplored due to the strict conditions required for their formation. Here, in this study, we report the synthesis of ternary platinum-nickel-phosphorus (PtNiP) amorphous nanoparticles (ANPs) within milliseconds by flash Joule heating, which features ultrafast cooling that enables the vitrification of metal precursors. Through compositional optimization, the Gibbs free energy of hydrogen adsorption for Pt 4 Ni 4 P 1 ANPs is optimized at 0.02 eV, an almost ideal value, even surpassing that of the benchmark metallic platinum catalyst. As a result, the PtNiP ANPs exhibited superior activity in electrocatalytic hydrogen evolution in acid electrolyte (η 10 ∼ 14 mV, Tafel slope ∼ 18 mV dec –1 , and mass activity 5× higher than state-of-the-art Pt/C). Life-cycle assessment and technoeconomic analysis suggest that, compared to existing processes, our approach enables notable reductions in greenhouse gas emission, energy consumption, and production cost for practical electrolyzer catalyst manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Structure-aware methods for expensive derivative-free nonsmooth composite optimization

We present new methods for solving a broad class of bound-constrained nonsmooth composite minimization problems. These methods are specially designed for objectives that are some known mapping of outputs from a computationally expensive function. We provide accompanying implementations of these methods: in particular, a novel manifold sampling algorithm (MS-P) with subproblems that are in a sense primal versions of the dual problems solved by previous manifold sampling methods and a method (GOOMBAH) that employs more difficult optimization subproblems. For these two methods, we provide rigorous convergence analysis and guarantees. We demonstrate extensive testing of these methods. Open-source implementations of the methods developed in this manuscript can be found at https://github.com/POptUS/ IBCDFO/.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1986

Composition optimization of self-lubricating chromium carbide-based composite coatings for use to 760 deg C

This paper describes new compositions of self-lubricating coatings that contain chromium carbide. A bonded chromium carbide was used as the base stock because of the known excellent wear resistance and the chemical stability of chromium carbide. Additives were silver and barium fluoride/calcium fluoride eutectic. The coating constituents were treated as a ternary system consisting of: (1) the bonded carbide base material, (2) silver, and (3) the eutectic. A study to determine the optimum amounts of each constituent was performed. The various compositions were prepared by powder blending. The blended powders were then plasma sprayed onto superalloy substrates and diamond ground to the desired coating thickness. Friction and wear studies were performed at temperatures from 25 to 760 C in helium and hydrogen. A variety of counterface materials were evaluated with the objective of discovering a satisfactory metal/coating sliding combination for potential applications such as piston ring/cylinder liner couples for Stirling engines.

Dellacorte, C.↗

NASA NTRS1987

Composition optimization of self-lubricating chromium-carbide-based composite coatings for use to 760 C

Dellacorte, Chris↗

DOE OSTI2025

Autonomous alloy composition optimization using molecular dynamics guided by a large language model

Here, we present an autonomous materials discovery framework that couples a large language model (LLM) with molecular dynamics (MD) simulations to optimize Fe–Cr–Mn alloy compositions for tensile strength. Starting from six distinct compositions, the LLM operated as an intelligent agent, iteratively proposing changes based on prior simulation results and constraints. Over 50 iterations per case, the LLM adaptively explored the composition space, identifying high-strength regions, not easily accessible by conventional methods. The highest strength, 18.7 GPa, was achieved with Fe 71 Cr 25 Mn 4 composition, identified from a Fe 75 Cr 20 Mn 5 starting point. The LLM autonomously adjusted its strategy in real time, demonstrating closed-loop decision-making using commodity hardware. This approach showcases the potential of LLMs as scientific co-pilots, capable of accelerating materials discovery and generalizable to other domains like biology and drug design.

Autonomy↗

NASA NTRS1988

Tribological composition optimization of chromium-carbide-based solid lubricant coatings for foil gas bearings at temperatures to 650 C

The determination of the tribilogically optimum composition of chromium-carbide-based solid lubricant coatings using a foil gas bearing test apparatus is described. The coatings contain a wear resistant chromium carbide `base stock' with the lubricant additives silver and BaF2-CaF2 eutectic. The coating composition is optimized for air-lubricated foil gas bearings at temperatures ranging from 25 to 650 C. The various compositions were prepared by powder blending, then plasma sprayed onto Inconel 718 test journals and diamond ground to the desired coating thickness and surface finish. The journals were operated against preoxidized Ni-Cr alloy foils, and the test bearings were subjected to repeated start-stop cycles under a bearing unit of 14 kPa. Sliding contact between the coated journal and the smooth foil occurs during bearing start-up before lift-off or hydrodynamic lubrication by the air film and during bearing coast-down. The bearings were tested for 9000 start-stop cycles or until specimen reached a predetermined failure level.

Dellacorte, Christopher↗

DOE OSTI2023

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.

Mohanty, Trupti (ORCID:0000000342701430)↗

NASA NTRS1987

Composition optimization of chromium carbide based solid lubricant coatings for foil gas bearings at temperatures to 650 C

A test program to determine the optimum composition of chromium carbide based solid lubricant coatings for compliant gas bearings is described. The friction and wear properties of the coatings are evaluated using a foil gas bearing test apparatus. The various coatings were prepared by powder blending, then plasma sprayed onto Inconel 718 test journals and diamond ground to the desired coating thickness and surface finish. The journals were operated against preoxidized nickel-chromium alloy foils. The test bearings were subjected to repeated start/stop cycles under a 14 kPa (2 psi) bearing unit load. The bearings were tested for 9000 start/stop cycles or until the specimen wear reached a predetermined failure level. In general, the addition of silver and eutectic to the chromium carbide base stock significantly reduced foil wear and increased journal coating wear. The optimum coating composition, PS212 (70 wt% metal bonded Cr3C2, 15 wt% Ag, 15% BaF2/CaF2 eutectic), reduced foil wear by a factor of two and displayed coating wear well within acceptable limits. The load capacity of the bearing using the plasma-sprayed coating prior to and after a run-in period was ascertained and compared to polished Inconel 718 specimens.

Dellacorte, Christopher↗

NASA NTRS2018

Development and Application of a Tool for Optimizing Composite Matrix Viscoplastic Material Parameters

This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is presented wherein the combined effects of temperature and loading rate on the predicted response of a braided composite is investigated.

thermo-mechanical; MATLAB; GUI-Based Too↗

DOE OSTI2023

Glass formulation and composition optimization with property models: A review

Abstract Glass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.

Lu, Xiaonan↗

DOE OSTI2025

Resin Testing and Modeling for Optimal Composite Processing

Polymer composites have properties such as high strength and stiffness, low weight, good thermal and chemical stability, as well as impact and abrasion resistance that make them ideal for high-performance applications. The chemistries of these materials are continuously improving, so determining their properties is vital for successfully producing them and achieving the desired results. Multiple methods can be employed to monitor characteristics such as heat flow, weight, dimension, and modulus as a function of time and temperature. By analyzing this information, models can be developed to predict outcomes of parameters not tested for. In one application, materials proposed for wet filament winding and the production of high pressure vessels can be analyzed to verify they will have the necessary low viscosity for good fiber wetting and long pot life for the extended handling inherent to this process. Such data about a prospective system provides valuable information on how that material could ultimately be processed to yield the desired part.

36 MATERIALS SCIENCE↗

NASA NTRS1973

Development of improved asbestos reinforced phenolic insulating composites (optimization of physical properties as a function of molding technique and post cure conditions)

Detailed data are presented on phenolic-glass and phenolic-asbestos compounds which compare the effect of compression molding without degas to the effects of four variations of compression molding. These variations were designed to improve elimination of entrapped volatiles and the volatile products of the condensate reaction associated with the cure of phenolic resins. The utilization of conventional methods of degas plus degas by vacuum and directional heat flow methods are involved. Detailed data are also presented on these same compounds, comparing the effect of changes in post-bake time, and post-bake temperature for the five molding techniques.

Hedges, L. M.↗

NASA NTRS1992

Modeling of Euclidean braided fiber architectures to optimize composite properties

Three-dimensional braided fiber reinforcements are a very effective toughening mechanism for composite materials. The integral yarn path inherent to this fiber architecture allows for effective multidirectional dispersion of strain energy and negates delamination problems. In this paper a geometric model of Euclidean braid fiber architectures is presented. This information is used to determine the degree of geometric isotropy in the braids. This information, when combined with candidate material properties, can be used to quickly generate an estimate of the available load-carrying capacity of Euclidean braids at any arbitrary angle.

Armstrong-Carroll, E.↗

NASA NTRS2006

Optimal Composite Material for Low Cost Fabrication of Large Composite Aerospace Structures using NASA Resins or POSS Nanoparticle Modifications

Thermoplastic laminates in situ consolidated via tape or tow placement require full mechanical properties. Realizing full properties requires resin crystallinity to be controlled - partial crystallinity leads to unacceptably low laminate compression properties. There are two approaches: utilize an amorphous matrix resin; or place material made from a semi-crystalline resin featuring kinetics faster than the process. In this paper, a matrix resin evaluation and trade study was completed with commercial and NASA amorphous polyimides on the one hand, and with PEKK mixed with POSS nanoparticles for accelerated crystallinity growth on the other. A new thermoplastic impregnated material, 6 mm wide (0.25-in) AS-4 carbon/LaRC(TradeMark)8515 dry polyimide tow, was fabricated. Since LaRC(TradeMark)8515 is fully amorphous, it attains full properties following in situ consolidation, with no post processing required to build crystallinity. The tow in situ processing was demonstrated via in situ thermoplastic filament winding it into rings.

Lamontia, Mark A.↗

DOE OSTI2023

Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m −1 K −2 , which is a 75% improvement from the baseline composite (nominal composition of Ag 2 Se 1 ). In conclusion, the success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

36 MATERIALS SCIENCE↗

NASA NTRS1982

Optimization of composite structures

Structural optimization is introduced and examples which illustrate potential problems associated with optimized structures are presented. Optimized structures may have very low load carrying ability for an off design condition. They tend to have multiple modes of failure occurring simultaneously and can, therefore, be sensitive to imperfections. Because composite materials provide more design variables than do metals, they allow for more refined tailoring and more extensive optimization. As a result, optimized composite structures can be especially susceptible to these problems.

Stroud, W. J.↗

NASA NTRS1983

Optimization of composite structures

Stroud, W. J.↗