arXiv subjects

Adrien Couet

Publications and source records attributed to Adrien Couet.

12 recordsLinked to original sources

arXiv2026

First-principles-based Prediction of Phase Fields: Part I. Binary and Ternary Refractory Alloys

Multiple principal element alloys (MPEAs) exhibit complex phase equilibria involving multinary solid solutions and intermetallics, which makes it challenging to predict their temperature-composition phase diagrams. Their vast compositional space makes first principles methods prohibitively expensive, while CALPHAD is limited by scarce experimental data. Here, we present a computationally efficient framework to predict the solvus phase boundaries, and hence, phase fields, in refractory MPEAs composed of Cr, Hf, Mo, Nb, Ta, Ti, V, W, and Zr. The approach combines DFT calculated binary mixing enthalpies with sub regular solution models to construct phase diagrams without fitting higher order interactions, enabling efficient scaling across composition space. Validation against 36 binary and 15 ternary phase diagrams demonstrates good agreement, with both experimental results and CALPHAD calculations. We find that the prediction accuracy is enhanced by incorporating lattice dependent energetics through sub regular solution models and including temperature-dependent elemental phase transitions. The framework captures miscibility gaps, solid solution stability, and intermetallic formation, with predicted miscible temperatures typically within 300 K of experimental values. Overall, this work establishes a scalable, first principles based route for highthroughput prediction of phase diagrams in refractory MPEAs. A publicly accessible web interface has also been developed to allow interactive exploration of the predicted phase diagrams, available at https://raptor.engr.wustl.edu.

cond-mat.mtrl-sci↗

arXiv2026

Role of diffusion-induced grain boundary migration during molten salt corrosion of a Ni-30Cr alloy

The response of Ni-Cr alloys to exposure to molten chloride and fluoride salts is typically characterized by Cr dealloying with the formation of a Cr-depleted bi-continuous porous subsurface layer. The exact mechanism behind the loss of Cr over distances unattainable by lattice diffusion alone is still debated. To address this question, two different surface finishes, namely electropolished and sanded, of a Ni-30Cr alloy were exposed to LiCl-KCl-2wt% EuCl3 eutectic salt at 500 {\deg}C for 96 hours. In the absence of fast diffusion pathways, dissolution occurred layer by layer and was kinetically controlled by Ni dissolution, as observed over the grain interiors of the electropolished sample. Grain boundaries were subject to diffusion-induced grain boundary migration (DIGM), leading to the formation of pure Ni islands above grain boundaries. This overall behavior contrasted with the sanded surface response that was characterized by several micrometer deep interconnected porosity and complete Cr depletion. DIGM of the dense grain boundaries created by recrystallization of the sanded surface was responsible for the observed sub-surface microstructure. This work unequivocally establishes DIGM as a key mechanism in alloy molten salt corrosion, and microstructure as a decisive contributor to an alloy's corrosion response.

cond-mat.mtrl-sci↗

arXiv2026

Charge redistribution at metal-ZrO2 interfaces: A combined DFT and continuum electrostatic study

Nanoscale metallic inclusions (NMIs) are commonly observed within oxide scales formed during high-temperature oxidation, revealing the existence of chemical and electronic heterogeneity beyond conventional corrosion theories that assume homogeneous, fully oxidized films. Using tetragonal zirconia (tZrO2) facing a series of face-centered cubic (fcc) metals as the model system, this work investigates the short-range and long-range charge redistributions across metal-oxide interfaces by coupling density functional theory (DFT) calculations with continuum modeling. We show that metal-oxide contact induces a short-range charge redistribution confined to a few atomic layers and a long-range redistribution of space charge that can extend over macroscopic distances within weakly doped oxides. DFT calculations show that the short-range redistribution is dominated by metal induced gap states (MIGS) in tZrO2 facing noble metals like Au and Ag, and by chemical bonding in tZrO2 facing active metals like Al. DFT-informed continuum theoretical analysis shows that the range of space-charge redistribution is governed by the doping level of tZrO2, and that the Schottky barrier height (SBH) exhibits a stronger dependence on the metal work function than the doping level. Both the short-range and long-range charge redistributions can alter the transport of charge carriers via their associated electric fields, extending several nm to hundreds of nm from the interface, depending on the doping concentrations, suggesting possible heterogeneous oxide growth caused by NMIs.

cond-mat.mtrl-sci↗

arXiv2026

Mapping optical, chemical, structural features in ZrO2 via cross-sectional SEM-Cathodoluminescence correlation microscopy

Understanding how nanoscale heterogeneities influence charge transport and mass transfer in oxides is critical for developing advanced materials for energy and electronic uses. In high-temperature applications, the formation of thermal oxides with complex chemical and structural features plays a central role in material lifetime. While thermally grown zirconia (ZrO2) on zirconium alloys exhibits strong chemical and microstructural gradients across the oxide thickness, linking these heterogeneities to electronic-defect landscapes remains challenging. We demonstrate cross-sectional scanning electron microscope-cathodoluminescence (SEM-CL) as a mesoscale probe of spatial variations in luminescence in zirconia and establish correlations with co-registered electron backscatter diffraction (EBSD) and electron probe micro-analysis (EPMA) on the same region. The SEM-CL signal is dominated by the ~2.7 eV defect band, but its intensity varies strongly across the oxide cross section. Correlative EBSD-CL analysis reveals that CL intensity increases with grain area and decreases at the grain boundaries, consistent with enhanced non-radiative recombination associated with microstructural disorder. EPMA mapping shows that a substantial fraction of CL-dark features co-localize with secondary phase precipitates enriched in iron. These results show that SEM-CL contrast in corrosion-grown ZrO2 is controlled by both chemical heterogeneity and microstructural disorder, underscoring the need for correlative registration to interpret CL images. This multi-modal approach provides an efficient route to connect electronic properties and luminescence signatures across complex oxide cross sections to underlying chemistry and microstructure, thereby providing a pathway to relate local defect landscapes to regions likely to bias electronic/ionic transport during oxidation.

cond-mat.mtrl-sci↗

arXiv2025

High-throughput validation of phase formability and simulation accuracy of Cantor alloys

High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 {\deg}C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement.

cond-mat.mtrl-sci↗

arXiv2025

SymPlex Plots for Visualizing Properties in High-Dimensional Alloy Spaces

Conventional visualization tools such as phase diagrams and convex hulls are ill-suited to visualize multiple principal element alloys (MPEAs) due to their large compositional space that cannot be easily projected onto two dimensions. Here, SymPlex plots are introduced to enable the visualization of various properties along special paths in high-dimensional phase spaces of MPEAs. These are polar heatmaps that plot properties along high-symmetry paths radiating from the parent equimolar MPEA to a set of chosen lower-order compositions. SymPlex plots capture the changes in the energy landscape along the special paths and help visualize the effect of addition or substitution of components on the alloy stability, which can be especially useful to assess processing pathways for additive manufacturing. Thus, SymPlex plots can help guide design of MPEAs by showing connections between compositions and their properties in the high-dimensional phase space with more information concentrated near the equimolar region.

cond-mat.mtrl-sci↗

arXiv2024

Lewis Acidity and Basicity Diagnostics of Molten Salt for its Properties and Structure Online Monitoring

Analogous to the aqueous solution where the pH of the solvent affects its multiple behaviors, the Lewis acidity-basicity of molten salts also greatly influences their thermophysical and thermochemical properties. In the study, we develop ion probes to quantitatively determine the acidity-basicity scale of molten NaCl-xAlCl3 (x = 1.5-2.1) salt using in-situ ultra-violet visible (UV-Vis) spectroscopy. With the accumulation of acidity-basicity data of NaCl-AlCl3 molten salt for a variety of compositions, the correlation between the acidity-basicity of salt and its measured fundamental properties are derived. To understand the physical and chemical features controlling the acidity-basicity variations, the structures of NaCl-xAlCl3 molten salts with different chemical compositions are investigated in terms of bonded complexes and coordination numbers. The comprehensive understanding of the correlation between composition, acidity-basicity, properties, and structures of molten salt can serve for the full screening and online monitoring of salt melt in extreme environments by simply measuring the salt acidity-basicity as developed in this study.

cond-mat.mtrl-sci↗

arXiv2023

Multi-principal element alloy discovery using directed energy deposition and machine learning

Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy design. Additive manufacturing via directed energy deposition is demonstrated as a high-throughput technique for synthesizing alloys in the Cr-Fe-Mn-Ni quaternary system. More than 100 compositions are synthesized in a week, exploring a broad range of compositional space. Uniform compositional control to within +/-5 at% is achievable. The rapid synthesis is combined with conjoint sample heat treatment (25 samples vs 1 sample), and automated characterization including X-ray diffraction, energy-dispersive X-ray spectroscopy, and nano-hardness measurements. The datasets of measured properties are then used for a predictive strengthening model using an active machine learning algorithm that balances exploitation and exploration. A learned parameter that represents lattice distortion is trained using the alloy compositions. This combination of rapid synthesis, characterization, and active learning model results in new alloys that are significantly stronger than previous investigated alloys.

cond-mat.mtrl-sci↗

arXiv2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature gradients, stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeup, with multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that based on Gaussian process and Bayesian optimization. We show ML examples in structural materials ( e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. This review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends in artificial intelligence (AI) and machine learning will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

cond-mat.mtrl-sci↗

arXiv2022

Novel Materials and Concepts for Next-Generation High Power Target Applications

Novel beam-intercepting materials and targetry concepts are essential to improve the performance, reliability and operation lifetimes of next generation multi-megawatt (multi-MW) accelerator target facilities. The beam-intercepting materials and components must sustain an order-of-magnitude increase in particle beam intensities and are beyond the current state-of-the-art. With conventional materials already limiting the scope of experiments, it is crucial to investigate novel target materials, technologies and concepts that will satisfy the requirements and maximize the physics benefits of future energy and intensity frontier experiments. This paper provides an overview of the related targetry R&D required over the next 10 years to support and enable future high-power accelerator target facilities.

physics.acc-ph↗

arXiv2021

A First-Principles-Based Approach to The High-Throughput Screening of Corrosion-Resistant High Entropy Alloys

The design of corrosion-resistant high entropy alloys (CR-HEAs) is challenging due to the alloys' virtually astrological composition space. To facilitate this, efficient and reliable high-throughput exploratory approaches are needed. Toward this end, the current work reports a first-principles-based approach exploiting the correlations between work function, surface energy, and corrosion resistance (i.e., work function and surface energy are, by definitions, proportional and inversely proportional to an alloy's inherent corrosion resistance, respectively). Two Bayesian CALPHAD models (or databases) of work function and surface energy of FCC Co-Cr-Fe-Mn-Mo-Ni are assessed using discrete surface energies and work functions derived by density-functional theory (DFT) calculations. The models are then used to rank different Co-Cr-Fe-Mn-Mo-Ni alloy compositions. It is observed that the ranked alloys possess chemical traits similar to previously studied corrosion-resistance alloys, suggesting that the proposed approach can be used to reliably screen HEAs with potentially good inherent corrosion resistance.

cond-mat.mtrl-sci↗

arXiv2021

Accelerated Discovery of Molten Salt Corrosion-resistant Alloy by High-throughput Experimental and Modeling Methods Coupled to Data Analytics

Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for molten salt applications and develop fundamental understanding of corrosion in these environments, here we present an integrated approach using a set of high-throughput alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning. By using this approach, a broad range of Cr-Fe-Mn-Ni alloys were evaluated for their corrosion resistances in molten salt simultaneously demonstrating that corrosion-resistant alloy development can be accelerated by thousands of times. Based on the obtained results, we unveiled a sacrificial mechanism in the corrosion of Cr-Fe-Mn-Ni alloys in molten salts which can be applied to protect the less unstable elements in the alloy from being depleted, and provided new insights on the design of high-temperature molten salt corrosion-resistant alloys.

cond-mat.mtrl-sci↗