arXiv subjects

Rodrigo Freitas

Publications and source records attributed to Rodrigo Freitas.

At least 19 recordsLinked to original sources

arXiv2026

Chemical short-range order controls deformation pathways in a complex concentrated alloy

Chemical short-range order (CSRO) is an intrinsic feature of complex concentrated alloys (CCAs), yet its influence on deformation mechanisms is controversial because of the inconclusive state of concurrent CSRO quantification during deformation. Here, we provide experimental evidence that CSRO acts as an intrinsic thermodynamic state variable governing stacking-fault energetics and deformation pathways in a Co30Cr40Ni30 alloy. By comparing quenched (CSRO-lean) and aged (CSRO-enriched) conditions with equivalent grain structure and phase constitution, we isolate the influence of atomic-scale chemical ordering on mechanical behavior. Calorimetry confirms reversible CSRO formation, while synchrotron X-ray diffraction and electron microscopy reveal that CSRO suppresses deformation-induced fcc-hcp martensitic transformation at both room and cryogenic temperatures. Despite differences in transformation dynamics, the macroscopic tensile response is still broadly similar. Atomistic simulations show that CSRO increases both stable and unstable stacking-fault energies, raising the energetic barrier for partial-dislocation activity and stabilizing the fcc lattice against transformation. Together, the experimental and computational results establish CSRO as an added degree of freedom for tuning stacking-fault energetics and controlling deformation pathways in complex concentrated alloys.

cond-mat.mtrl-sci↗

arXiv2026

Precipitation strengthening: a collective multi-dislocation phenomenon

Precipitation strengthening is a cornerstone of physical metallurgy, delivering otherwise unattainable combinations of strength and ductility. The approach relies on nanoscale precipitates that impede the motion of dislocations, the primary carriers of plastic deformation. Historically, precipitation strengthening has been rationalized via two idealized, limiting mechanisms: dislocations either cut through or bow around precipitates. However, in situ experiments cannot yet resolve the coupled, real-time evolution of dislocation networks and nanoprecipitates, leaving these atomic-scale dynamics inaccessible to direct observation. Here, using large-scale atomistic simulations that fully capture these dynamics, we demonstrate that the classical cutting-versus-bowing dichotomy is incomplete. Instead, strengthening arises as an emergent collective phenomenon driven by concurrent, multi-dislocation interactions. These interactions simultaneously induce dislocation accumulation at interfaces, storage within precipitates, and precipitate-mediated multiplication inside the matrix. These findings establish a mechanistic framework that transcends traditional models and provides a new foundation for predicting strengthening behavior.

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arXiv2026

Complementary Thermodynamic Mechanisms of Boron and Carbon Segregation at Grain Boundaries in Nickel Alloys

Grain boundary stabilization by light interstitials is central to the performance of Ni-based superalloys, yet the thermodynamic mechanisms governing their interactions with substitutional chemistry remain poorly resolved. Here, we use hybrid Monte Carlo molecular dynamics simulations to quantify how boron and carbon modify the thermodynamic, structural, and chemical ordering of grain boundaries in Ni--Cr alloys. By analyzing interfacial state variables, site-resolved segregation spectra, local chemical ordering, and structural evolution, we show that boron and carbon stabilize grain boundaries through complementary pathways. Carbon drives saturation-controlled stabilization by recruiting Cr and conditioning boundary chemistry, while suppressing temperature-driven structural transformations of the boundary. In contrast, boron stabilizes grain boundaries through a selective mechanism that lowers the interfacial grand potential via localized ordering while permitting gradual structural evolution. These effects arise from coupled interactions between interstitial segregation and Cr redistribution, which together regulate site accessibility, chemical competition, and the range of accessible interfacial states. This work provides a thermodynamic framework for grain boundary engineering and suggests design principles for leveraging interstitial-substitutional interactions in alloys.

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arXiv2026

Direct Observation of Chemical Short-Range Order in CoCrNi Alloy Using Neutron Diffraction

This study provides experimental evidence of chemical short-range order (CSRO) in the equiatomic CoCrNi alloy, identified through neutron diffraction. The phenomenon manifests as a distinct diffuse peak at Q = 1.85 A-1, the intensity increases under thermodynamically favorable conditions for CSRO development such as prolonged aging (100 h and 240 h) at 748 K or shorter aging (24 h) at slightly higher temperature (798 K). The degree of ordering was measured by integrating the diffuse scattering intensity, revealing that the gas-atomized sample, i.e. the sample with the least amount of CSRO, still displays approximately 70% of the CSRO level observed in the sample subsequently aged for 240 h at 748 K, i.e. the sample with the highest amount of CSRO produced in this study. Predictive atomistic simulations reproduced both the presence and position of the diffuse peak, while two-dimensional fast Fourier transform (FT-2D) analyses indicated that reflections at (1 1/2 0) within the <001> zone axis originate from some structural projections associated with like D022, Pt2Mo and D1a motifs. Complementary small-angle neutron scattering (SANS) measurements identified Ni-rich, disk-shaped domains with radii of approximately 11 A and thicknesses of about 1 A, consistent with nanoscale CSRO characteristic length scale. These findings demonstrate that CSRO is an intrinsic and energetically favorable feature of the CoCrNi system, remaining stable even under rapid solidification and further enhanced by low-temperature aging. Combined use of neutron diffraction and atomistic modeling provides a framework for probing local ordering phenomena in multi-principal element alloys (MPEAs).

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arXiv2026

Detecting the Onset and Progression of Spinodal Decomposition using Transient Grating Spectroscopy

Spinodal decomposition can degrade corrosion resistance and embrittle materials. The ability to quickly, conclusively, and non-destructively detect the onset of spinodal decomposition before catastrophic materials degradation would represent a significant advance in materials testing. We demonstrate that spinodal decomposition can be detected in binary Fe-Cr alloys via modulus stiffening using in situ and ex situ transient grating spectroscopy (TGS). The key mechanistic insight is the non-linearity in elastic moduli as function of Cr content renders a spinodally decomposed Fe-Cr alloy stiffer than an equivalent solid solution for a certain range of initial chromium compositions. We confirm the presence of spinodal decomposition in the 36 at.% chromium alloy using differential scanning calorimetry (DSC), linked to known spinodal decomposition energetics, and show via atomistic simulations that elastic modulus stiffening is expected after spinodal decomposition in the 36 at.% chromium alloy. The results of this study suggest the potential use of TGS as a practical tool for non-destructive evaluation of key materials susceptible to such degradation.

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arXiv2026

Stable Machine Learning Potentials for Liquid Metals via Dataset Engineering

Liquid metals are central to energy-storage and nuclear technologies, yet quantitative knowledge of their thermophysical properties remains limited. While atomistic simulations offer a route to computing liquid properties directly from atomic motion, the most accurate approach, ab initio molecular dynamics (AIMD), is computationally costly and restricted to short time and length scales. Machine learning interatomic potentials (MLPs) offer AIMD accuracy at far lower cost, but their application to liquids is limited by training datasets that inadequately sample atomic configurations, leading to unphysical force predictions and unstable trajectories. Here we introduce a physically motivated dataset-engineering strategy that constructs liquidlike training data synthetically rather than relying on AIMD configurations. The method exploits the established icosahedral short-range order of metallic liquids, twelvefold, near-close-packed local coordination, and generates "synthetic-liquid" structures by systematic perturbation of crystalline references. MLPs trained on these datasets close the sampling gaps that lead to unphysical predictions, remain numerically stable across temperatures, and reproduce experimental liquid densities, diffusivities, and melting temperatures for multiple elemental metals. The framework links atomic-scale sampling to long-term MD stability and provides a practical route to predictive modeling of liquid-phase thermophysical behavior beyond the limits of direct AIMD.

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arXiv2025

Spectral Sampling of Boron Diffusion in Ni Alloys: Cr and Mo Effects on Bulk and Grain Boundary Transport

Understanding how light interstitials migrate in chemically complex alloys is essential for predicting defect dynamics and long-term stability. Here, we introduce a spectral sampling framework to quantify boron diffusion activation energies in Ni and demonstrate how substitutional solutes (Cr, Mo) reshape interstitial point defect transport in both the bulk and along crystallographic defects. In the bulk, boron migration energy distributions exhibit distinct modality tied to solute identity and spatial arrangement: both Cr and Mo raise barriers in symmetric cages but induce directional asymmetry in partially decorated environments. Extending this framework to a $\Sigma5\langle100\rangle{210}$ symmetric tilt grain boundary reveals solute-specific confinement effects. Cr preserves low-barrier in-plane mobility while suppressing out-of-plane transport, guiding boron into favorable midplane voids. Mo, by contrast, imposes an across-the-board reduction in boron mobility, suppressing average diffusivity by two additional orders of magnitude at 800 $^\circ$C and reducing out-of-plane transport by five orders of magnitude relative to Cr. Both elements promote segregation by producing negative segregation energies, but their roles diverge: Cr facilitates rapid redistribution and stabilization at interfacial sites, consistent with Cr-rich boride formation, while Mo creates deeper and more uniform segregation wells that strongly anchor boron. Together, these complementary behaviors explain the experimental prevalence of Cr- and Mo-rich borides at grain boundaries and carbide interfaces in Ni-based superalloys. More broadly, we establish spectral sampling as a transferable framework for interpreting diffusion in disordered alloys and for designing dopant strategies that control transport across complex interfaces.

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arXiv2025

Atomistic mechanisms of oxidation and chlorine corrosion in Ni-based superalloys: The role of boron and light interstitial segregation

Hybrid Monte Carlo and molecular dynamics simulations were used to investigate the interaction of light interstitials in multi-element Ni-based alloys. We show that light interstitials such as boron and oxygen fundamentally alter interfacial chemistry by reshaping alloy-element distribution and segregation. Oxygen adsorption drove boron migration from the grain boundary to the free surface, where it co-enriched with Cr, Fe, and Mo and formed BO3 trigonal motifs embedded within mixed-metal oxide networks. Oxygen also promoted M-O-M chain formation, including Nb2O5 clusters at the free surface. In the absence of oxygen, boron segregated to the grain boundary, altering local metal chemistry and underscoring a dynamic, environment-sensitive behavior. Following chlorine exposure, the oxidized surfaces retained strong O-mediated connectivity while forming new Cl-M associations, particularly with Nb and Cr, and exhibited further surface enrichment in Cr, Fe, and Mo. High-temperature MD simulations revealed a dynamic tug-of-war: chlorine exerted upward pull and disrupted weakly anchored sites, while Nb- and BO3-rich oxide motifs resisted deformation. A new stabilization mechanism was identified in which subsurface boron atoms anchored overlying Cr centers, suppressing their mobility and mitigating chlorine-driven displacement. These results demonstrate boron's dual role as a modifier of alloy-element segregation and a stabilizer of oxide networks, and identify Nb as a key element in reinforcing cohesion under halogen attack. More broadly, this study highlights the need to track light interstitial cross-talk and solute migration under reactive conditions, offering atomistic criteria for designing corrosion-resistant surface chemistries in Ni-based superalloys exposed to halogenated or oxidative environments.

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arXiv2025

Spectral Analysis of Light Interstitial Segregation Energies in Ni: The Role of Local Cr Coordination for Boron and Carbon

Understanding interstitial segregation in chemically complex alloys requires accounting for chemical and structural heterogeneity of interfaces, motivating approaches that move beyond scalar descriptors to capture the full spatial and compositional spectra of segregation behavior. Here, we introduce a spectral segregation framework that maps distributions of segregation energies for light interstitials in Ni as a function of local Cr coordination. Boron exhibits a broad, rugged energy spectrum with significant positional flexibility whereas carbon remains confined to a narrow spectrum with minimal displacement. At the free surface, Cr-rich coordination destabilizes both interstitials (e.g., positive segregation energies), in sharp contrast to the stabilizing role of Cr at the GB. This inversion establishes a natural segregation gradient that drives interstitials away from undercoordinated internal surfaces and toward GBs. These results underscore the limitations of single-valued segregation descriptors and demonstrate how a distributional approach reveals the mechanistic origins of interstitial--interface interactions in chemically heterogeneous alloys.

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arXiv2025

Dislocation-mediated short-range order evolution during thermomechanical processing

Thermomechanical processing alters the microstructure of metallic alloys through coupled plastic deformation and thermal exposure, with dislocation motion driving plasticity and microstructural evolution. Our previous work (Islam et al., 2025) showed that the same dislocation motion both creates and destroys chemical short-range order (SRO), driving alloys into far-from-equilibrium SRO states. However, the connection between this dislocation-mediated SRO evolution and processing parameters remains largely unexplored. Here, we perform large-scale atomistic simulations of thermomechanical processing of equiatomic TiTaVW to determine how temperature and strain rate control SRO via competing creation ($\Gamma$) and annihilation ($\lambda$) rates. The simulations employ systems containing 2.4 million atoms and utilize a machine learning interatomic potential optimized to capture chemical complexity through the motif-based sampling technique. Using information-theoretic metrics, we quantify that the magnitude and chemical character of SRO vary systematically with processing parameters. We identify two regimes: a low-temperature regime with weak strain-rate sensitivity, and a high-temperature regime in which reduced dislocation density and increased screw character amplify chemical bias and accelerate SRO formation. The resulting steady-state SRO is far-from-equilibrium and cannot be produced by equilibrium thermal annealing. Together, these results provide a mechanistic and predictive link between processing parameters, dislocation physics, and SRO evolution in chemically complex alloys.

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arXiv2025

Segregation and Ordering of Light Interstitials (B, C, H, and N) in Cr-Ni Alloys: Implications for Grain Boundary Stability in Superalloy Design

The segregation and ordering behavior of light interstitials (B, C, and N) in Cr30-Ni is examined, as these elements are critical for grain boundary stability and high-temperature mechanical performance in Ni-based superalloys. Using Monte Carlo simulations, we identify the chemical and structural preferences of these interstitials in both bulk and grain boundary (GB) environments, aligning with experimental segregation and precipitation trends. Boron strongly prefers GBs over the bulk, where it enhances GB cohesion and stabilizes the GB structure. Uniquely, boron induces a structural transformation at higher concentrations, hinting at the formation of serrated GBs where boron content is high, which improves high-temperature mechanical performance. Carbon and nitrogen form carbide and nitride motifs and exhibit limited GB solubility, reinforcing their precipitation tendencies. In support of ongoing hydrogen embrittlement mitigation strategies, we also examined hydrogen behavior. Hydrogen demonstrated chemical stability in the Cr-Ni GB zone, suggesting it may preferentially migrate inward along Cr- and Ni-rich GBs while avoiding Mo-enriched regions, further supporting Mo's role in mitigating embrittlement. These findings suggest that Mo-containing borides may serve as effective barriers against hydrogen-induced degradation by inhibiting H ingress and stabilizing GB cohesion. By elucidating the chemical and structural preferences of these light interstitials, this work provides a robust computational framework for guiding superalloy design toward improved high-temperature grain boundary stability, resistance to hydrogen embrittlement, and controlled chemical ordering.

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arXiv2025

Atomistic Simulations of Short-range Ordering with Light Interstitials in Inconel Superalloys

This study employed hybrid Monte Carlo Molecular Dynamics simulations to investigate the short-range ordering behavior of Ni-based superalloys doped with boron or carbon. The simulations revealed that both boron and carbon dissociated from their host Ti atoms to achieve energetically favored ordering with Cr, Mo, and Nb. Boron clusters formed as B2, surrounded by Mo, Nb, and Cr, while carbon preferentially clustered with Cr to form a Cr23C6 local motif and with Nb to form Nb2C. Distinct preferences for interstitial sites were observed, with boron favoring tetrahedral sites and carbon occupying octahedral sites. In the presence of a vacancy, B2 shifted from the tetrahedral site to the vacancy, where it remained coordinated with Mo, Nb, and Cr. Similarly, carbon utilized vacancies to form Nb2C clusters. Excess energy calculations showed that B and C exhibited strong thermodynamic stability within their short-range ordered configurations. However, under Ti-rich conditions, C was more likely to segregate into TiC, despite preexisting ordering with Cr. This shift in stability suggests that increased Ti availability would alter carbide formation pathways, drawing C away from Cr-rich networks and promoting the development of TiC. Such redistribution may disrupt the continuity of Cr-based carbide networks, which play a critical role in stabilizing grain boundaries and impeding crack propagation. These effects further underscore the impact of interstitial-induced ordering on phase stability and microstructural evolution. This work provides an atomistic perspective on how boron- and carbon-induced ordering influences microstructure and mechanical properties. These findings highlight the critical role of interstitial-induced short-range ordering and demonstrate that this mechanism can be leveraged as a design principle to fine-tune alloy microstructures for specific engineering applications.

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arXiv2025

Machine learning potentials for modeling alloys across compositions

Materials properties depend strongly on chemical composition, i.e., the relative amounts of each chemical element. Changes in composition lead to entirely different chemical arrangements, which vary in complexity from perfectly ordered (i.e., stoichiometric compounds) to completely disordered (i.e., solid solutions). Accurately capturing this range of chemical arrangements remains a major challenge, limiting the predictive accuracy of machine learning potentials (MLPs) in materials modeling. Here, we combine information theory and machine learning to optimize the sampling of chemical motifs and design MLPs that effectively capture the behavior of metallic alloys across their entire compositional and structural landscape. The effectiveness of this approach is demonstrated by predicting the compositional dependence of various material properties - including stacking-fault energies, short-range order, heat capacities, and phase diagrams - for the AuPt and CuAu binary alloys, the ternary CrCoNi, and the TiTaVW high-entropy alloy. Extensive comparison against experimental data demonstrates the robustness of this approach in enabling materials modeling with high physical fidelity.

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arXiv2024

Nonequilibrium chemical short-range order in metallic alloys

Metallic alloys are routinely subjected to nonequilibrium processes during manufacturing, such as rapid solidification and thermomechanical processing. It has been suggested in the high-entropy alloy literature that chemical short-range order (SRO) could offer a new knob to tailor materials properties. While evidence of the effect of SRO on materials properties accumulates, the state of SRO evolution during alloy manufacturing remains obscure. Here, we employ high-fidelity atomistic simulations to track SRO evolution during the solidification and thermomechanical processing of alloys. Our investigation reveals that alloy processing can lead to nonequilibrium steady-states of SRO that are different from any equilibrium state. The mechanism behind nonequilibrium SRO formation is shown to be an inherent ordering bias present in nonequilibrium events. These results demonstrate that conventional manufacturing processes provide pathways for tuning SRO that lead to a broad nonequilibrium spectrum of SRO states beyond the equilibrium design space of alloys.

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arXiv2024

Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks

Crystalline materials have atomic-scale fluctuations in their chemical composition that modulate various mesoscale properties. Establishing chemistry-microstructure relationships in such materials requires proper characterization of these chemical fluctuations. Yet, current characterization approaches (e.g., Warren-Cowley parameters) make only partial use of the complete chemical and structural information contained in local chemical motifs. Here we introduce a framework based on E(3)-equivariant graph neural networks that is capable of completely identifying chemical motifs in arbitrary crystalline structures with any number of chemical elements. This approach naturally leads to a proper information-theoretic measure for quantifying chemical short-range order (SRO) in chemically complex materials, and a reduced - but complete - representation of the chemical space. Our framework enables the correlation of any per-atom property with their corresponding local chemical motif, thereby offering novel avenues to explore structure-property relationships in chemically-complex materials. Using the MoTaNbTi high-entropy alloy as a test system, we demonstrate the versatility of this approach by evaluating the lattice strain associated with each chemical motif, and computing the temperature dependence of chemical-fluctuations length scale.

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arXiv2024

Capturing short-range order in high-entropy alloys with machine learning potentials

Chemical short-range order (SRO) affects the distribution of elements throughout the solid-solution phase of metallic alloys, thereby modifying the background against which microstructural evolution occurs. Investigating such chemistry-microstructure relationships requires atomistic models that act at the appropriate length scales while capturing the intricacies of chemical bonds leading to SRO. Here we consider various approaches for the construction of training data sets for machine learning potentials (MLPs) for CrCoNi and evaluate their performance in capturing SRO and its effects on materials quantities of relevance for mechanical properties, such as stacking-fault energy and phase stability. It is demonstrated that energy accuracy on test sets often does not correlate with accuracy in capturing material properties, which is fundamental in enabling large-scale atomistic simulations of metallic alloys with high physical fidelity. Based on this analysis we systematically derive design principles for the rational construction of MLPs that capture SRO in the crystal and liquid phases of alloys.

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arXiv2023

Quantifying chemical short-range order in metallic alloys

Metallic alloys often form phases - known as solid solutions - in which chemical elements are spread out on the same crystal lattice in an almost random manner. The tendency of certain chemical motifs to be more common than others is known as chemical short-range order (SRO) and it has received substantial consideration in alloys with multiple chemical elements present in large concentrations due to their extreme configurational complexity (e.g., high-entropy alloys). Short-range order renders solid solutions "slightly less random than completely random", which is a physically intuitive picture, but not easily quantifiable due to the sheer number of possible chemical motifs and their subtle spatial distribution on the lattice. Here we present a multiscale method to predict and quantify the SRO state of an alloy with atomic resolution, incorporating machine learning techniques to bridge the gap between electronic-structure calculations and the characteristic length scale of SRO. The result is an approach capable of predicting SRO length scale in agreement with experimental measurements while comprehensively correlating SRO with fundamental quantities such as local lattice distortions. This work advances the quantitative understanding of solid-solution phases, paving the way for SRO rigorous incorporation into predictive mechanical and thermodynamic models.

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arXiv2022

The Uhlenbeck-Ford model: Exact virial coefficients and application as a reference system in fluid-phase free-energy calculations

The Uhlenbeck-Ford (UF) model was originally proposed for the theoretical study of imperfect gases, given that all its virial coefficients can be evaluated exactly, in principle. Here, in addition to computing the previously unknown coefficients B11 through B13, we assess its applicability as a reference system in fluid-phase free-energy calculations using molecular simulation techniques. Our results demonstrate that, although the UF model itself is too soft, appropriately scaled Uhlenbeck- Ford (sUF) models provide robust reference systems that allow accurate fluid-phase free-energy calculations without the need for an intermediate reference model. Indeed, in addition to the accuracy with which their free energies are known and their convenient scaling properties, the fluid is the only thermodynamically stable phase for a wide range of sUF models. This set of favorable properties may potentially put the sUF fluid-phase reference systems on par with the standard role that harmonic and Einstein solids play as reference systems for solid-phase free-energy calculations.

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