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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
DOE OSTI2025

Mixed-Flux Techniques for Rational Synthesis and Structural Control in Silver Chalcogenides

The functionality of materials is intrinsically linked to their structures, an axiom encapsulated in the principle of structure-property relationships. The pinnacle of materials design is the tailoring of its structure for a specific function, which requires the ability of rational synthesis and the development of synthesis science. This idea, however, remains elusive for the synthesis of complex extended solids. A major obstacle is the difficulty in using established chemical principles selectively to control reaction paths and favor certain structural patterns over numerous other possible results. In this context, we are developing a synthesis science approach that facilitates the control of the structure and bonding to create new structures. This is achieved by employing a two-component flux consisting of mixed hydroxides and halides as the reaction medium. This enables reaction conditions that allow better control of the structure dimensionality and composition by manipulating the temperature and solvent basicity (via the flux component ratio). Here, we demonstrate the efficacy of this method in controlling their structural motifs to arrive at 23 unreported compositions and 6 unique structure types. These materials are expected to exhibit a broad range of properties, from metallic to semiconducting, with calculations suggesting the potential for emergent phenomena such as Dirac semimetals. The reaction paths afforded by these mixed fluxes establish a direct correlation between the synthetic variables and properties, providing significant insight into a broadly applicable approach for new materials.

Zhou, Xiuquan [Argonne National Laboratory (ANL), ↗
DOE OSTI2026

Comparison of the Arrhenius parameters between conventional hydrothermal and microwave-assisted synthesis methods for tin oxide nanoparticles

Microwave (MW) irradiation has emerged as a powerful tool for accelerating materials synthesis, yet the origins of its specific influence on reaction kinetics remain elusive. While multiple studies have attributed the observed enhancements in reaction rates under MW heating to reduced activation energies, other accounts have suggested modifications to the Arrhenius pre-exponential factor as the predominant cause. Distinguishing between these parameters in modern applications of MW processing in nanomaterials requires experimental approaches capable of resolving the dynamic and nuanced structural kinetics that govern MW-assisted chemistry. Here, we combine in-situ synchrotron X-ray total scattering with pair distribution function (PDF) analysis to track the structural evolution of SnO 2 nanoparticles synthesized via MW-assisted and conventional hydrothermal conditions. Avrami modeling and Arrhenius analysis suggest that although MW irradiation yields a higher apparent activation energy, the enhanced crystallization is better explained by a pre-exponential factor several orders of magnitude larger than that of conventional heating. These findings suggest that the MW field induces a higher frequency of successful molecular rearrangements rather than lowering the intrinsic activation barrier. The results contribute further insights clarifying the role of the applied MW field for materials design where MW-specific effects can be deliberately harnessed. Furthermore, this work presents a framework promoting the utility of in-situ PDF characterization coupled with kinetic analysis for developing more sophisticated descriptions of nanoscale transformations for MW-driven reaction kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Defects go green: using defects in nanomaterials for renewable energy and environmental sustainability

Induction of point defects in nanomaterials can bestow upon them entirely new physics or augment their pre-existing physical properties, thereby expanding their potential use in green energy technology. Predicting structure-property relationships for defects a priori is challenging, and developing methods for precise control of defect type, density, or structural distribution during synthesis is an even more formidable task. Hence, tuning the defect structure to tailor nanomaterials for enhanced device performance remains an underutilized tool in materials design. We review here the state of nanomaterial design through the lens of computational prediction of defect properties for green energy technology, and synthesis methods to control defect formation for optimal performance. We illustrate the efficacy of defect-focused approaches for refining nanomaterial physics by describing several specific applications where these techniques hold potential. Most notably, we focus on quantum dots for reabsorption-free solar windows and net-zero emission buildings, oxide cathodes for high energy density lithium-ion batteries and electric vehicles, and transition metal dichalcogenides for electrocatalytic green hydrogen production and carbon-free fuels.

14 SOLAR ENERGY↗
DOE OSTI2022

A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Numerical and Experimental Study of a Novel Additively Manufactured Metal-Polymer Composite Heat-Exchanger for Liquid Cooling Electronics

In order to meet increasing power-dissipation requirements of the electronics industry, compact, low-cost, and lightweight heat exchangers (HXs) are desired. With proper design, materials, and manufacture, polymer composite heat exchangers could meet these requirements. This paper presents a novel crossflow air-to-water, low-cost, and lightweight metal-polymer composite HX. This HX, which is entirely additively manufactured, utilizes a novel cross-media approach that provides direct heat exchange between air and liquid sides by using connecting fins. A robust numerical model was developed, which includes the dimensional effects of additive manufacturing. The study consists of a simplified 3D CFD model based on ellipsoidal-shaped staggered tube banks for the laminar range. It then uses an analytical approach to compute entire HX performance. The model is validated experimentally within 8% for thermal performance, 12% for air-side impedance, and 18% for water-side impedance. Finally, HX is compared with a conventional CPU radiator and performs within 10% of the conventional unit for reasonable flow rates and pressure-drop ranges. Moreover, HX also provides added design and cost advantages over the conventional unit, which makes the HX a potential candidate for electronic cooling applications.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Fluorophilic Sigma(σ)‐Lock Self‐Healable Copolymers

Abstract Although F‐Containing molecules and macromolecules are often used in molecular biology to increase the binding with Lewis acidic groups by introducing favorable C−F dipoles, there is virtually no experimental evidence and limited understanding of the nature of these interactions, especially their role in synthetic polymeric materials. These studies elucidate the molecular origin of inter‐ and intra‐Chain interactions responsible for self‐healing of F‐Containing copolymers composed of pentafluorostyrene and n‐butyl acrylate units (p(PFS/nBA). Guided by dynamic surface oscillating force (SOF) and spectroscopic measurements supported by molecular dynamics (MD) simulations, these studies show that the reformation of σ‐σ orbitals in −C−F of PFS and CH 3 CH 2 − of nBA units enables the recovery of entropic energy via fluorophilic‐σ‐lock van der Waals forces when PFS/nBA molar ratios are ~50/50. The strength of these interactions determined experimentally for self‐healable PFS/nBA compositions is in the order ~0.3 kcal/mol which primarily comes from fluorophilic‐σ‐lock (~70 %) contributions. These interactions are significantly diminished for non‐self‐healable counterparts. Strongly polarized −C−F σ orbitals create lateral dipolar forces enhancing the affinity towards −C−H orbitals, facilitating energetically favorable interactions. Entropic recovery driven by non‐Covalent bonding offers a valuable tool in designing materials with unique functionalities, particularly self‐healable batteries and energy storage devices.

Gaikwad, Samruddhi↗
DOE OSTI2021

In situ mechanical reinforcement of polymer hydrogels via metal-coordinated crosslink mineralization

Biological organic-inorganic materials remain a popular source of inspiration for bioinspired materials design and engineering. Inspired by the self-assembling metal-reinforced mussel holdfast threads, we tested if metal-coordinate polymer networks can be utilized as simple composite scaffolds for direct in situ crosslink mineralization. Starting with aqueous solutions of polymers end-functionalized with metal-coordinating ligands of catechol or histidine, here we show that inter-molecular metal-ion coordination complexes can serve as mineral nucleation sites, whereby significant mechanical reinforcement is achieved upon nanoscale particle growth directly at the metal-coordinate network crosslink sites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Flutter behavior of highly flexible blades for two- and three-bladed wind turbines

Abstract. With the progression of novel design, material, and manufacturing technologies, the wind energy industry has successfully produced larger and larger wind turbine rotor blades while driving down the levelized cost of energy (LCOE). Though the benefits of larger turbine blades are appealing, larger blades are prone to aeroelastic instabilities due to their long, slender, highly flexible nature, and this effect is accentuated as rotors further grow in size. In addition to the trend of larger rotors, non-traditional rotor concepts are emerging including two-bladed rotors and downwind configurations. In this work, we introduce a comprehensive evaluation of flutter behavior including classical flutter, edgewise vibration, and flutter mode characteristics for two-bladed, downwind rotors. Flutter speed trends and characteristics for a series of both two- and three-bladed rotors are analyzed and compared in order to illustrate the flutter behavior of two-bladed rotors relative to more well-known flutter characteristics of three-bladed rotors. In addition, we examine the important problem of blade design to mitigate flutter and present a solution to mitigate flutter in the structural design process. A study is carried out evaluating the effect of leading edge and trailing edge reinforcement on flutter speed and hence demonstrates the ability to increase the flutter speed and satisfy structural design requirements (such as fatigue) while maintaining or even reducing blade mass.

Chetan, Mayank (ORCID:0000000241978801)↗
DOE OSTI2024

Fluorophilic Sigma(σ)‐Lock Self‐Healable Copolymers

Although F-Containing molecules and macromolecules are often used in molecular biology to increase the binding with Lewis acidic groups by introducing favorable C−F dipoles, there is virtually no experimental evidence and limited understanding of the nature of these interactions, especially their role in synthetic polymeric materials. These studies elucidate the molecular origin of inter- and intra-Chain interactions responsible for self-healing of F-Containing copolymers composed of pentafluorostyrene and n-butyl acrylate units (p(PFS/nBA). Guided by dynamic surface oscillating force (SOF) and spectroscopic measurements supported by molecular dynamics (MD) simulations, these studies show that the reformation of σ-σ orbitals in −C−F of PFS and CH 3 CH 2 − of nBA units enables the recovery of entropic energy via fluorophilic-σ-lock van der Waals forces when PFS/nBA molar ratios are ~50/50. The strength of these interactions determined experimentally for self-healable PFS/nBA compositions is in the order ~0.3 kcal/mol which primarily comes from fluorophilic-σ-lock (~70 %) contributions. These interactions are significantly diminished for non-self-healable counterparts. Strongly polarized −C−F σ orbitals create lateral dipolar forces enhancing the affinity towards −C−H orbitals, facilitating energetically favorable interactions. Entropic recovery driven by non-Covalent bonding offers a valuable tool in designing materials with unique functionalities, particularly self-healable batteries and energy storage devices.

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

Tailoring additive manufacturing to optimize dynamic properties in 316L stainless steel

With the advent of additive manufacturing, manipulation of typical microstructural elements such as grain size, texture, and defect densities is now possible at a faster time scale. While the processing–structure–property relationship in additive manufactured metals has been well studied over the past decade, little work has been done in understanding how this process affects the dynamic behavior of materials. We postulate that additive manufacturing can be used to alter the material microstructure and used to enhance its dynamic strength. In this work, 316L stainless steel (SS) was manufactured via selected laser melting and its microstructure was altered through changing build parameters like laser power, speed, and hatch spacing systematically. These samples were then subjected to spall recovery experiments to measure the spall strength and quantify the amount of damage as a function of build parameters. By mapping the spall strength as a function of build parameters, this work demonstrated that indeed additive manufacturing can be used to tailor the spall strength of 316L SS. This work also determined the optimum build parameters (laser power=195W; scanning speed=1083mm/s; hatch spacing=0.09mm; layer thickness=0.02mm) to obtain the highest spall strength and the least amount of total damage in 316L SS. Microstructural characterization of the pre- and post-mortem samples revealed that increased grain average misorientation and textural index were the main driving force behind this higher spall strength. This work aims to enhance microstructural engineering techniques to design materials with greater resistance to dynamic shock loading.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Preliminary Characterization and Evaluation on ShAPE Manufactured 316H and ODS Steels

This study provides the first- of- a- kind results of direct tube formation through shear assisted processing and extrusion (ShAPE) for oxide dispersion strengthened (ODS) steel material; previously only bar was successfully made. The Advanced Materials and Manufacturing Technology (AMMT) program develops cross-cutting technologies in support of a broad range of nuclear reactor technologies and maintains U.S. leadership in materials and manufacturing technologies for nuclear energy applications. The overarching vision of AMMT is to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. Solid-state advanced manufacturing techniques can overcome some of the challenges in liquid-based additive manufacturing processes and should therefore be considered in material design and manufacturing as well. The work presented in this report forms part of a study on solid-state additive manufacturing techniques of 316 stainless steels and ODS steel components and supports the vision and goals of the AMMT program relevant to accelerate the development and deployment of advanced manufacturing processes. Achieving this can provide a safety improvement through larger safety margins, economic benefit for higher efficiency during operation, and a cost reduction through more effective manufacturing processes and less waste.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning-accelerated discovery of iron cobalt phosphides as rare-earth-free magnets

Here, the discovery of rare-earth-free permanent magnets has been a goal of scientists for decades. The absence of rare-earth elements will alleviate a pressing concern about the availability of rare-earth elements used in permanent magnets. These magnets are crucial for applications such as wind turbines, electric cars, and memory devices. Rare-earth magnets are special owing to a large magnetic anisotropy energy (K 1 ). In contrast, iron cobalt phosphides hold promise since doping P into cubic FeCo can induce anisotropy, leading to a large coercivity, without introducing rare-earth elements. We present a comprehensive search over the Fe-Co-P ternary space for magnets, utilizing recently developed adaptive machine learning feedback to efficiently screen over 850 000 structures. We focus on machine learning acceleration as a paradigm for materials design. Further adaptive genetic algorithm searches and first-principles calculations aid in the identification of 16 new structures below the known convex hull. Five of them possess high magnetic polarization (J s > 1 T). The structures with desirable magnetic properties center on (Fe,Co) 2⁢ P. This supports conventional wisdom, which focuses on the mixture of the two known end compounds: Fe 2 ⁢P and Co 2 ⁢P. Our work provides guidance for synthesis. We find Fe 7 ⁢CoP 4 shows the most promise (J s = 1.03T and K 1 = 0.83MJ/m 3 ).

36 MATERIALS SCIENCE↗
DOE OSTI2022

The 2021 room-temperature superconductivity roadmap

Designing materials with advanced functionalities is the main focus of contemporary solid-state physics and chemistry. Research efforts worldwide are funneled into a few high-end goals, one of the oldest, and most fascinating of which is the search for an ambient temperature superconductor (A-SC). The reason is clear: superconductivity at ambient conditions implies being able to handle, measure and access a single, coherent, macroscopic quantum mechanical state without the limitations associated with cryogenics and pressurization. This would not only open exciting avenues for fundamental research, but also pave the road for a wide range of technological applications, affecting strategic areas such as energy conservation and climate change. In this roadmap we have collected contributions from many of the main actors working on superconductivity, and asked them to share their personal viewpoint on the field. The hope is that this article will serve not only as an instantaneous picture of the status of research, but also as a true roadmap defining the main long-term theoretical and experimental challenges that lie ahead. Interestingly, although the current research in superconductor design is dominated by conventional (phonon-mediated) superconductors, there seems to be a widespread consensus that achieving A-SC may require different pairing mechanisms.

"Toward hot superconductivity"↗
DOE OSTI2025

Utilizing Single-Crystalline Transformations for Precise Atom Placement in Multicomponent Cluster-Based Coordination Networks

The assembly of cluster or superatom building-blocks into extended solids has revolutionized materials design, enabling the synthesis of modular semiconductors with well-defined structures and tunable electronic, magnetic or optical properties. This strategy has recently advanced the synthesis of complex metal oxides with multifunctional or emergent behaviors, but precise atom placement of multiple elements with similar chemistries or preferred coordination environments remains a significant challenge. Here, in this study, we present a strategy for synthesizing polyoxometalate (POM)-based coordination networks with up to three different cations in precisely defined positions. Our approach leverages a single-crystal-to-single-crystal (SCSC) transformation in which the spatial placement of cations is governed by their availability at distinct stages of crystallization and transformation. Specifically, [ZP 5 W 30 O 110 ] (15-n)- (Z = Na + , K + , Ca 2+ , Ag + , Bi 3+ , Y 3+ , any Ln 3+ , Th 4+ ) is coordinatively assembled with various bridging metal cations (Y 3+ , any Ln 3+ , Th 4+ ). By using the encapsulated cation (Z) to "label" the POM, we track the phase-transformation and confirm the retention of single crystallinity. The integrated use of POM labeling and SCSC transformation enables rational control over cation distribution and establishes a versatile strategy for constructing multicomponent materials with high compositional and spatial precision.

Chen, Linfeng [Univ. of California, San Diego, CA ↗
DOE OSTI2025

Database of Nonaqueous Proton-Conducting Materials

This work presents the assembly of 48 papers, representing 74 different compounds and blends, into a machine-readable database of nonaqueous proton-conducting materials. SMILES was used to encode the chemical structures of the molecules, and we tabulated the reported proton conductivity, proton diffusion coefficient, and material composition for a total of 3152 data points. The data spans a broad range of temperatures ranging from -70 to 260 °C. To explore this landscape of nonaqueous proton conductors, DFT was used to calculate the proton affinity of 18 unique proton carriers. The results were then compared to the activation energy derived from fitting experimental data to the Arrhenius equation. It was found that while the widely recognized positive correlation between the activation energy and proton affinity may hold among closely related molecules, this correlation does not necessarily apply across a broader range of molecules. This work serves as an example of the potential analyses that can be conducted using literature data combined with emerging research tools in computation and data science to address specific materials design problems.

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

Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene

Here, we introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algorithm that is general and scalable and presents an efficient multiobjective optimization scheme for high-dimensional materials design problems. As a proof-of-concept, we deploy this scheme to parametrize multiple structural and dynamical properties of 2D phosphorene polymorphs. Our RL-trained BOP model adequately captures the structure, energetics, transformation barriers, equation of state, elastic constants, and phonon dispersions of various 2D P polymorphs. We use this model to probe the impact of temperature and strain rate on the phase transition from black (α-P) to blue phosphorene (β-P) through molecular dynamics simulations. A decrease in critical strain for this phase transition with increase in temperature is observed, and the underlying atomistic mechanisms are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗