Optimized Alloy Composites Final Technical Report
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21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
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Computational design of thermoelectric alloys through optimization of transport and dopability
Alloying is a common technique to optimize the functional properties of materials for thermoelectrics, photovoltaics, energy storage etc. Designing thermoelectric (TE) alloys is especially challenging because it is a multi-property optimization problem, where the properties that contribute to high TE performance are interdependent. In this work, we develop a computational framework that combines first-principles calculations with alloy and point defect modeling to identify alloy compositions that optimize the electronic, thermal, and defect properties. We apply this framework to design n-type Ba 2(1–x) Sr 2x CdP 2 Zintl thermoelectric alloys. Our predictions of the crystallographic properties such as lattice parameters and site disorder are validated with experiments. To optimize the conduction band electronic structure, we perform band unfolding to sketch the effective band structures of alloys and find a range of compositions that facilitate band convergence and minimize alloy scattering of electrons. Here, we assess the n-type dopability of the alloys by extending the standard approach for computing point defect energetics in ordered structures. Through the application of this framework, we identify an optimal alloy composition range with the desired electronic and thermal transport properties, and n-type dopability. Such a computational framework can also be used to design alloys for other functional applications beyond TE.
36 MATERIALS SCIENCE↗
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↗
Thermo-mechanical behavior of hypoeutectic Ni-Y-Zr alloys
Microstructure refinement and optimized alloying can improve metallic alloy performance: stable nanocrystalline (NC) alloys with immiscible second phases, e.g., Cu-Ta, are stronger than unstable NC alloys and their coarse-grained (CG) counterparts, but higher melting point matrices are needed. Hypoeutectic, CG Ni-Y-Zr alloys were produced via arc-melting to explore their potential as high-performance materials. Microstructures were studied to determine phases present, local composition and length scales, while heat treatments allowed investigating microstructural stability. Alloys had a stable, hierarchical microstructure with ~250 nm ultrafine eutectic, ~10 µm dendritic arm spacing and ~1 mm grain size. Hardness and uniaxial compression tests revealed that mechanical properties of Ni-0.5Y-1.8Zr (in wt%) were comparable to Inconel 617 despite the small alloying additions, due to its hierarchical microstructure. Here, uniaxial compression at 600 °C showed that ternary alloys outperformed Ni-Zr and Ni-Y binary alloys in flow stress and hardening rates, which indicates that the Ni 17 Y 2 phase was an effective reinforcement for the eutectic, which supplemented the matrix hardening due to increased solubility of Zr. Results suggest that ternary Ni-Y-Zr alloys hold significant promise for high temperature applications.
36 MATERIALS SCIENCE↗
Friction surface layer deposition of triple-phase Al 10 Cr 12 Fe 35 Mn 23 Ni 20 high entropy alloy: Process optimization and microstructural evolution
A high-strength Co-free triple-phase Al 10 Cr 12 Fe 35 Mn 23 Ni 20 high-entropy alloy (HEA) was successfully fabricated using Friction Surface Layer Deposition (FSLD), a bulk manufacturing method. Multiple single-layer deposits were produced by varying forging force (F) and traverse speeds (T r ) to optimize the process parameters. The optimized conditions (F = 40 kN & T r = 200 mm/min) were then applied to manufacture a scaled-up multi-layer specimen. The initial microstructure of the HEA consisted of coarse grains of the soft FCC-phase, long columnar dendrites of the hard BCC-phase, and small precipitates of the harder B2-phase within the BCC-dendrites. During FSLD, the FCC-matrix underwent continuous dynamic recrystallization due to high-temperature severe plastic deformation, forming finer equiaxed grains. Simultaneously, the BCC-dendrites fractured into smaller fragments, some of which experienced partial growth and coarsening under applied stress, resulting in an hourglass morphology. In contrast, the small B2-precipitates within the BCC-fragments dissolved during the elevated temperatures of FSLD and reprecipitated as substantially finer precipitates during continuous cooling post-FSLD. Additionally, the orientation relationships between the FCC and BCC/B2 phases were completely destroyed by the severe thermoplastic deformation inherent to FSLD. The microstructural refinements led to a substantial improvement in hardness from 177 HV to 283 HV, driven by Hall-Petch strengthening. The increased number of interfaces, including coherent BCC-B2 interfaces, potentially enhances the sink strength and radiation tolerance of the HEA, making it a promising candidate for nuclear applications. In conclusion, this study also highlights FSLD as a versatile technique for achieving tunable properties in HEAs, with detailed schematics illustrating the complex mechanisms of phase transformations during processing.
Additive Manufacturing↗
P‐Doping Modulated RuIr Nanoparticles Anchored on Co/N/C Catalysts with Improved Alkaline Hydrogen Evolution Activity and Stability
Achieving efficient and stable hydrogen evolution reactions in alkaline conditions is crucial for hydrogen production. In this study, a RuIr/Co (SA) NC-P catalyst featuring RuIr alloys alongside P-doping and CoNx sites is developed. RuIr alloying optimizes the electronic structure between Ru and Ir, promoting electron transfer from Ru to Ir. P-doping further modulates the electronic properties of RuIr alloys, optimizing hydrogen binding energy and weakening Ru─OH binding energy, facilitating rapid H 2 generation and OH ad transfer. Meanwhile, CoNx promotes water dissociation, providing a fast proton delivery path for RuIr alloys. The catalyst exhibits enhanced HER activity with a low overpotential of 20 mV at 10 mA cm −2 , a Tafel slope of 20.4 mV dec −1 , and a turnover frequency of 19.5 H 2 s −1 at 150 mV overpotential. Moreover, catalyst stability is improved 8 times by mitigating RuIr alloy dissolution/agglomeration via P-doping. In conclusion, this work introduces a promising approach for developing efficient and stable HER electrocatalysts.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Efficient Modeling of Structural, Electronic, and Optical Properties of Silver and Gold Metal Nanoclusters and Alloys Using Optimized SCC-DFTB Parameters
Computation of optical properties using conventional time-dependent density functional theory (TD-DFT) is time-consuming and memory-intensive. In this study, we investigate the accuracy and efficiency of the density functional tight binding (DFTB) framework with newly optimized Slater–Koster (SK) parameters for modeling the structural, electronic properties, and absorption spectra of silver and gold nanoclusters and their alloys. Our investigation of the ground state (GS) properties demonstrates that the newly developed GS-SK parameters enable DFTB to closely approximate DFT-calculated bond lengths for octahedron, tetrahedron, icosahedra, and truncated octahedron with sizes Ag n /Au n (n = 19, 20, 38, 55), nanoclusters and Ag 20 /Au 20 nanoalloys, with a maximum deviation of approximately 0.15 Å. Formation energy results indicate that the GS-SK parameters can closely estimate changes in formation energies with alloy composition, and the comparison of electronic structures for Ag 20 , Au 20 , and AgAu alloy nanoclusters using the DFTB approximation reveals good agreement in the projected density of states (DOS) profiles and energy levels. A second set of SK parameters, ES-SK, has been developed to describe excited state (ES) properties, including the absorption spectra of silver octahedron Ag 19 , tetrahedral Ag n (n = 20, 56, 84), truncated octahedron Ag 38 , and icosahedra Ag 55 closed-shell clusters and their gold and alloy counterparts over a broad range of alloy compositions. This parametrization uses TD-DFTB calculations and fine-tunes the d and p eigenvalues by comparing them to reference absorption spectra from first-principles TD-DFT. This enables the generation of absorption spectra that closely match the reference spectra when plasmon excitation is dominant, as demonstrated by studying the plasmonic properties of icosahedral Ag n and Au n (n = 309 and 561) nanoparticles. This includes the rapid loss in plasmon quality when Au partially replaces Ag in alloy clusters. Furthermore, these results provide a foundation for addressing computational bottlenecks in plasmonics and with new prospects for applications in the quantum plasmonics for bimetallic alloys.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accelerated Selectrion of Optimal Perovskite Alloys for Solar PV using a Combined Quantum and Machine Learning Hierachiral Approach
The project aims to: (i) accelerate the discovery of “Missing HP alloys” by combining quantum mechanics and artificial intelligence machine learning approaches, and (ii) analyze the stabilities of candidate alloys, including those that do not pass selection filters (and are thus expected to degrade over time) to decipher the nature of the instabilities to guide the development of durable solar cell materials. Successful candidates will be subjected to validation experiments at NREL's state-of-the-art facilities. The discoveries this effort will provide will be directly testable and implementable and will greatly impact U.S. progress in HP PV as they will provide clear direction and motivation for experimental studies including specific material synthetic targets, device optimization, and device stability protocols. A key advantage of this effort is the feedback and guidance provided by the Industry Collaborative Work Group that we established to coordinate academic and national lab research with industry needs. The proposed work will provide a basis for and direct the development of robust and reliable HP PV. It will also provide a timely, valuable and extensive roadmap to the experimental HP PV community to enable it to focus its efforts on improving and fine-tuning promising HP compositions that this effort predicts will likely be the best performers rather than wandering in the vast chemical space for decades spending enormous resources mostly evaluating unpromising candidate materials.
14 SOLAR ENERGY↗
Discovery and Optimization of Complex, Concentrated Alloys via Multi-Objective Bayesian Optimization and Multi-Scale Modeling
Presenting work on bayesian optimization of complex alloys
Startt, Jacob Kyle↗
Exceptional enhancement of mechanical properties in high-entropy alloys via thermodynamically guided local chemical ordering
Understanding the local chemical ordering propensity in random solid solutions, and tailoring its strength, can guide the design and discovery of complex, paradigm-shifting multicomponent alloys. First, we present a simple thermodynamic framework, based solely on binary enthalpies of mixing, to select optimal alloying elements to control the nature and extent of chemical ordering in high-entropy alloys (HEAs). Next, we couple high-resolution electron microscopy, atom probe tomography, hybrid Monte-Carlo, special quasirandom structures, and density functional theory calculations to demonstrate how controlled additions of Al and Ti and subsequent annealing drive chemical ordering in nearly random equiatomic face-centered cubic CoFeNi solid solution. We establish that short-range ordered domains, the precursors of long-range ordered precipitates, inform mechanical properties. Specifically, a progressively increasing local order boosts the tensile yield strengths of the parent CoFeNi alloy by a factor of four while also substantially improving ductility, which breaks the so-called strength–ductility paradox. Finally, we validate the generality of our approach by predicting and demonstrating that controlled additions of Al, which has large negative enthalpies of mixing with the constituent elements of another nearly random body-centered cubic refractory NbTaTi HEA, also introduces chemical ordering and enhances mechanical properties.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Modeling O and N Alloying in Nb for SRF Applications
N and O-alloyed superconducting radio frequency cavities exhibit extraordinary quality factors. Developing diffusion models that describe interstitial N and O in Nb is important for optimizing alloyed cavity quality factors and accelerating gradients. N and O-alloyed Nb samples are examined with SEM AND SIMS and their diffusion profiles modeled.
Lechner, Eric↗
MoNbTi-based Refractory Multi-principal Element Alloy System: A Review of the Thermodynamic Phase Predictions, Formed Microstructures, and Mechanical Properties as a Function of the Fabrication Methods (AM versus SPS versus VAM)
Abstract Multi-principal element alloys, particularly of refractory compositions, are an increasingly popular candidate for extreme-environment applications, including for next-generation nuclear reactors and in other industries, such as biomedical and aerospace, due to their high strength. The ability to achieve solid-solution microstructures provides these alloys with improved mechanical properties; however, these microstructures are heavily composition- and processing-dependent. In this review, the multi-principal element alloy MoNbTi-based system (with Zr, V, and Cr additions) was selected to compare the effects of elemental composition and processing route on the resulting microstructures and mechanical properties. The review provides insight into identifying the optimal alloy composition and processing route for a balance of desired microstructure and mechanical properties.
Krogh, Kara↗
Multiscale Development and Validation of the Stainless Steel Alloy Corrosion (SStAC) Tool for High Temperature Engine Materials
The goal of this project was to create a simulation tool to assist in part design, reducing costs by eliminating the need for conservative material selection, and enabling alloy optimization to improve corrosion resistance. Thus, it helps decrease costs for existing engines and assists in the material and engine design for the engines of the future. To meet our stated goal, we developed the Stainless-Steel Alloy Corrosion (SStAC) tool, which: (1) Models corrosion of engine valves fabricated from 21-2N valve steel in an engine environment at temperatures up to 800 °C. (2) Runs in 1D for a fast estimate of the corrosion rate, and in 2D or 3D for more detailed simulations that represent any part geometry and predict the precise location and rate of corrosion and its impact on the mechanical and thermal behavior. (3) Was implemented using the open-source Multiphysics Object Oriented Simulation Environment (MOOSE). (4) Couples the corrosion model with mechanics and thermal transport models. The SStAC tool can predict the corrosion rate of 21-2N engine valves with less than 10% error.
36 MATERIALS SCIENCE↗
Towards Automated Design of Corrosion Resistant Alloy Coatings with an Autonomous Scanning Droplet Cell
We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization. Automation and machine learning are currently driving rapid innovation in high-throughput and autonomous materials design and discovery. We present two alloy design vignettes: one focusing on a multi-objective corrosion resistant alloy optimization and a study highlighting the complexity of the multimodal characterization needed to provide insight into the underlying structural and chemical factors that drive observed material behavior. This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models. Lastly, we reflect on our early efforts in on-demand alloy deposition, highlighting some of the challenges. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design.
36 MATERIALS SCIENCE↗
Microstructure and mechanical properties of high Mn-containing ferritic-martensitic alloys exposed to cyclical thermal treatment
Substantial residual tensile stress tends to accumulate in currently available high-Cr ferritic martensitic steels that are subjected to cyclical heat treatment, which leads to premature brittle fracture. By tailoring the alloy composition, this thermal cycling can be exploited to induce a high number density of nanoprecipitates and phase transformations countering residual tensile stresses. In this work, three new variants of ferritic-martensitic steels have been designed with computational thermodynamics to meet the goals of mitigating residual tensile stresses by lowering martensite start temperatures and of enhancing mechanical strength and irradiation sink strength by increasing the number density of nanoprecipitates. Furthermore, ctenast materials were subjected to cyclical heat treatment. The thermally cycled samples were evaluated with mechanical testing and microstructural analysis to identify the optimal composition in which figures of merit include low residual stress and a high density of nanoscale MX (M = metal, X = C/N) precipitates, leading to high yield strength with reasonable ductility. The noticeably higher density of nanoprecipitates in the optimal alloy favor its higher yield strength, which is supported by the microstructure-derived yield strength calculation and precipitation kinetics simulation.
36 MATERIALS SCIENCE↗
Evaluation of crystalline quality of traveling heater method (THM) grown Cd0.9Zn0.1Te0.98Se0.02 crystals
Because of its excellent opto-electronic properties, CdZnTe (CZT) has been the material of choice for x- and gamma-ray detectors operable at room temperature. CZT is the leading commercially available room-temperature radiation detector material today. Although much progress has been made over the past three decades, today's CZT crystals still face certain challenges, especially the presence of the performance-limiting materials defects and the associated relatively high production cost. In this regard, CdxZn1−xTeySe1−y (CZTS) is emerging as a next-generation compound semiconductor, which overcomes some of the limitations of CZT technology for the stated applications. Here, we conducted a study to evaluate the crystalline quality of the traveling heater method grown CZTS with an optimized alloy composition, i.e., Cd0.9Zn0.1Te0.98Se0.02. The as-grown samples were evaluated by low-temperature photoluminescence (PL) spectroscopy and high-resolution x-ray diffraction using the synchrotron light source at Brookhaven National Laboratory. The full width at half maximum of both the PL and x-ray rocking curves was observed to be broadened due to the lattice disorder of the quaternary compound, eventually degrading the crystalline quality. This was consistent with density functional theory calculations.
Roy, U. N. (ORCID:0000000282089680)↗
Optical characterization of deep-level defects in n-type Al x In y Ga 1- x - y P for the development of solid-state photomultiplier analogs
Characterizing intrinsic defects is an essential step in evaluating materials for novel optoelectronic device applications. For photomultipliers, suppressing dark currents is critical, but a tradeoff is present between maximizing the band gap while remaining sensitive to the wavelength of interest and minimizing the incorporation of fresh defects by growing not-yet-optimized alloys. We present a series of capacitance-based measurements, including deep-level optical spectroscopy, steady-state photocapacitance, and illuminated capacitance-voltage, on photodiodes with lightly n-type Al x In y Ga 1−x−y P absorber regions. Several deep levels are identified, including one near the midgap. Although the inclusion of aluminum increases each trap density by approximately 10×, the hole capture cross-section also appears to decrease, suggesting that Shockley–Read–Hall dark currents may be suppressed. These materials may be good candidates for the development into silicon photomultiplier analogs with a wider bandgap for scintillator applications.
III–V↗
CALPHAD-based Bayesian optimization to accelerate alloy discovery for high-temperature applications
Two crucial properties influencing the performance of high-temperature alloys are coefficient of thermal expansion (CTE) and phase constitution. It is desirable to have alloys with low CTE, which reduces CTE mismatch with the surface oxide and the likelihood of oxide spallation. Reducing the amount of brittle intermetallic phases such as Sigma (σ) enhances alloy ductility and processability. Here, we propose a multi-objective Bayesian Optimization (BO) model to simultaneously minimize CTE (at an operational temperature of 1150 °C) and T σ (temperature when the Sigma phase completely dissolves in the metal matrix), properties which are obtained from high-throughput CALculation of PHAse Diagrams (CALPHAD). The model successfully identifies several alloys with CTE ≤ 2 × 10 –5 /K and T σ ≤ 500 °C by exploring just 7% of the nickel–chromium–cobalt–aluminum–iron (Ni–Cr–Co–Al–Fe) composition space. Such multi-objective alloy design frameworks can be used to inform additive manufacturing experiments and accelerate alloy discovery for high-temperature energy applications.
36 MATERIALS SCIENCE↗