arXiv subjects

Enrique Martinez

Publications and source records attributed to Enrique Martinez.

At least 19 recordsLinked to original sources

arXiv2026

Diffusion in a $d$-dimensional rough potential

The prediction of diffusion in solids is necessary to understand the microstructure evolution in materials out of equilibrium. Although one can reasonably predict diffusive transport coefficients using atomistic methods, these approaches can be very computationally expensive. In this work, we develop an analytical model for the diffusivity in a noisy solid solution in an arbitrary number of dimensions ($d$) using a mean first passage time analysis. We observe that roughness always decreases the diffusivity, aligning with sluggish diffusion theories in concentrated alloys, finding that an increase in diffusivity induced by alloying elements must be due to a decrease in the average activation energy, not to the noise. These analytical results are then compared with kinetic Monte Carlo simulations, which are in good quantitative agreement with the simulation data for $d\leq 5$, and excellent quantitative agreement for $d\leq 3$. This generalization to arbitrary dimensions has been elusive to the community since Zwanzig [PNAS, 85, 2029 (1988)] published his seminal work on 1-dimensional systems.

cond-mat.mtrl-sci↗

arXiv2026

First-principles transition-state tensorial cluster expansion of vacancy diffusion in Ta-W beyond the kinetically-resolved activation approximation

Predicting diffusion in chemically complex alloys remains challenging due to the strong dependence of migration barriers on local atomic environments. Migration barriers computed using density functional theory and nudged elastic band calculations are represented via a tensorial cluster expansion including transition states and deployed in on-lattice kinetic Monte Carlo simulations. Applied to the Ta-W system, the framework captures nontrivial composition-dependent diffusion behavior arising from a crossover between solute trapping and percolated low-barrier transport pathways, yielding a maximum in the apparent activation energy near intermediate compositions. This approach establishes a general and scalable route for integrating first-principles transition-state energetics into mesoscale kinetic simulations, enabling predictive multiscale modeling of diffusion in chemically complex materials and providing a pathway for uncovering emergent transport phenomena.

cond-mat.mtrl-sci↗

arXiv2026

Shrinkage-Constrained Functional Calibration for Complex Computer Models

We propose a new Bayesian model calibration formalism as an alternative to the Kennedy O'Hagan (KOH) framework which we term integrated bias with full uncertainty (IBFU). In KOH, calibration parameters are modeled as fixed, but unknown distributions with relatively weak prior constraints, and their posteriors are inferred jointly with an additive discrepancy Gaussian Process (GP). This formulation often provides limited regularization and leads to confounding pathologies when applied to inexact models with sparse, noisy measurements. By contrast, we represent each calibration parameter as the sum of a fixed best estimate value and a parameter correction represented by an independent GP over the input space, equipped with strong shrinkage priors. Any residual discrepancy that cannot be addressed via parameter correction is captured by an additive discrepancy GP operating on the simulator, similar to KOH. We then impose orthogonality constraints to mitigate confounding between the simulator and modeled additive discrepancy and colinearity between model parameters. Imposing strong complexity shrinkage via conservative hyperpriors forces the mean parameter correction to remain flat across the domain, resulting in predictions that essentially converge with the KOH formulation. However, upon relaxing complexity shrinkage, should the data provide evidence that the effective calibration parameter varies across the domain, the mean parameter correction is allowed to become a function of the domain in a controlled, structured manner. In this sense, our approach is more universal: it effectively nests KOH as a special case while extending it to input dependent calibration, and it is more tightly constrained, because it anchors the true values around the best estimates and the shrinkage prior actively regularizes the calibration parameters.

stat.ME↗

arXiv2026

A general statistical framework for vacancy and self-interstitial properties in concentrated multicomponent solids

A rigorous understanding of the thermodynamic properties of point defects, namely vacancies and self-interstitials, is crucial for the discovery and screening of structural materials in clean energy applications. In this work, we extend a previously-developed statistical framework for predicting the thermodynamics of single-site impurities to further predict the thermodynamics of self-interstitial dumbbells in an arbitrarily complex alloy. We then apply this extended framework to compute effective formation energies in fully disordered Fe-Cr and Cu-Ni alloys. Notably, we predict that some self-interstitial dumbbell types that are high-energy in pure Fe become stabilized by Cr. We additionally describe a symmetry-breaking effect, wherein high solute concentrations distort the defect free energy surface, yielding misaligned self-interstitials.

cond-mat.mtrl-sci↗

arXiv2026

A computationally efficient approach for predicting the transport properties of transition-metal alloys at elevated temperatures

A novel phenomenological framework for an efficient estimation of the thermo-electric properties at room temperature and elevated temperatures of body-centered cubic (BCC) transition metal concentrated alloys is proposed in this work. The methodology is used to predict the electrical resistivity of BCC systems with our predictions showing excellent correlation with experimental data. This framework is further extended to predict the electrical resistivity $ρ$, thermal conductivity $κ$ and the specific heat capacity Cp of BCC alloys in the temperature range of 300-1300 K and the results are validated against experimental data. We demonstrate the capabilities of this model by using it to predict the thermo-electric properties of a concentrated W53Ta42V5 alloy which shows a saturation in the electrical resistivity $ρ$ in the temperature range 300K-1300K. This model is then used to predict the properties of another concentrated Nb$_4$0Mo$_4$0Ta$_2$0 alloy in the same temperature regime.

cond-mat.mtrl-sci↗

arXiv2026

Active Learning of A Crystal Plasticity Flow Rule From Discrete Dislocation Dynamics Simulations

Continuum-scale material deformation models, such as crystal plasticity, can significantly enhance their predictive accuracy by incorporating input from lower-scale (i.e., mesoscale) models. The procedure to generate and extract the relevant information is however typically complex and ad hoc, involving decision and intervention by domain experts, leading to long development times. In this study, we develop a principled approach for calibration of continuum-scale models using lower scale information by representing a crystal plasticity flow rule as a Gaussian process model. This representation allows for efficient parameter space exploration, guided by the uncertainty embedded in the model through a process known as Bayesian optimization. We demonstrate a semi-autonomous Bayesian optimization loop which instantiates discrete dislocation dynamics simulations whose initial conditions are automatically chosen to optimize the uncertainty of a model crystal plasticity flow rule. Our self-guided computational pipeline efficiently generated a dataset and corresponding model whose error, uncertainty, and physical feature sensitivities were validated with comparison to an independent dataset four times larger, demonstrating a valuable and efficient active learning implementation readily transferable to similar material systems.

cond-mat.mtrl-sci↗

arXiv2025

Dependence of Radiation Induced Segregation of Cr on Sink Dimensionality and Morphology in Fe-Cr Alloys

Radiation-induced segregation (RIS) and chemical redistribution in structural alloys can significantly degrade material performance, ultimately leading to failure. In this study, building on previous work by the authors [1], we investigate how the dimensional characteristics of sinks influence solute concentration distributions and segregation behavior. Specifically, we utilize a kinetic Monte Carlo (KMC) model to simulate atomic-scale diffusion and analyze segregation processes in an Fe-3Cr alloy. Our analysis includes three representative sink geometries: one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) planar sinks to capture the effects of sink dimensionality on Cr segregation at grain boundaries (GBs). We also found solutions of concentration and segregation profiles in these cases as well as for a 3D spherical sink. KMC simulations are performed over a range of temperatures to assess thermal effects on Cr redistribution. The results reveal distinct segregation profiles and concentration gradients, although the dependence with sink density seems to remain linear in all cases with planar sinks. The analytical results show that this is not the case in spherical domains, with a more complex dependence of segregation on sink density. Our finite difference solutions for domains including 2D and 3D planer sinks show agreement with corresponding KMC results.

cond-mat.mtrl-sci↗

arXiv2025

A generalized and adaptable tensor-contraction-based cluster expansion formalism for multicomponent solids

Density functional theory (DFT)-based simulations of materials have first-principles accuracy, but are very computationally expensive. For simulating various properties of multi-component alloys, the cluster expansion (CE) technique has served as the standard workaround to improve computational efficiency. However, the standard CE technique is difficult to extend to exotic and/or low-symmetry lattices, often implemented via iteration over particular cluster types, which must be enumerated per lattice structure. In this work, we introduce the tensor cluster expansion (TCE), implemented in the open-source code tce-lib, which maps correlation functions to mixed tensor contractions, eliminating the need to iterate over cluster types and additionally making the calculation of correlation functions well-suited for massively parallel architectures like GPUs. We show that local interaction energies are an immediate consequence of the TCE formalism, yielding nearly $\mathcal{O}(1)$ energy difference calculations. We then use this formalism to fit CE models for the TaW and CoNiCrFeMn systems, and use these models to respectively compute the enthalpy of mixing curve and Cowley short-range order parameters, showing excellent agreement with ground truth data.

cond-mat.mtrl-sci↗

arXiv2025

Kinetic Monte Carlo prediction of the morphology of pentaerythritol tetranitrate

In this work, we develop an atomistic, graph-based kinetic Monte Carlo (KMC) simulation routine to predict crystal morphology. Within this routine, we encode the state of the supercell in a binary occupation vector and the topology of the supercell in a simple nearest-neighbor graph. From this encoding, we efficiently compute the interaction energy of the system as a quadratic form of the binary occupation vector, representing pairwise interactions. This encoding, coupled with a simple diffusion model for adsorption, is then used to model evaporation and adsorption dynamics at solid-liquid interfaces. The resulting intermolecular interaction-breaking energies are incorporated into a kinetic model to predict crystal morphology, which is implemented in the open-source Python package Crystal Growth Kinetic Monte Carlo (cgkmc). We then apply this routine to pentaerythritol tetranitrate (PETN), an important energetic material, showing excellent agreement with the attachment energy model.

cond-mat.mtrl-sci↗

arXiv2025

Thermal gradient effect on hydrogen transport in tungsten

One key challenge for efficiency and safety in fusion devices is the retention of tritium (T) in plasma-facing components. Tritium retention generates radioactive concerns and decreases the amount of fuel available to generate power. Hence, understanding the behavior of T in tungsten (W), as the main candidate as armor material, is critical to the deployment of fusion as a reliable energy source. In this work, we have studied the effect of a thermal gradient in the transport properties of hydrogen (as a T surrogate) in pure W. Strong thermal gradients develop in the divertor as a result of the intense energy fluxes arriving at the material. We have developed an analytical approach to compute the heat of transport ($Q^*$) that is parameterized from molecular dynamics (MD) simulations. $Q^*$ is a parameter needed in irreversible thermodynamics frameworks to understand mass transport in the presence of thermal gradients. We show that $Q^*$ can be written as a function of temperature, temperature gradient, a characteristic length and the ratio of the rates towards hot and cold regions. Furthermore, we describe how, to first order, the dependence of $Q^*$ on the thermal gradient vanishes, in agreement with MD results. On average, we find $Q^*=-5.41\times 10^{-3}kT^2~\text{eV}$ for H in pure W, with $k$ the Boltzmann constant and $T$ the temperature.

cond-mat.mtrl-sci↗

arXiv2025

Activation entropy helps explain anomalous flow stress temperature dependence in copper

Thermal activation of dislocations is critical for predicting the mechanical response of materials under common experimental conditions. According to transition state theory (TST), the rate for the system to overcome free energy barriers depends on an attempt frequency, activation free energy, and temperature. We computed the rate for edge and screw dislocation dipoles to overcome their interaction fields at various temperatures, Langevin friction coefficients, and shear stresses using Molecular Dynamics (MD), Schoecks entropy formalism and compared with Kramers rate theory. Kramers theory matches the rates computed dynamically, which depend on Langevin friction, increasing with weaker friction. Statically, using Schoeck formalism to compute the entropy along the minimum energy path (MEP), we found significant entropic effects that lead to an increase of the critical resolved shear stress with temperature and could help explain the long-standing anomaly observed at low to intermediate temperatures in copper and other metals, where the flow stress increases with temperature.

cond-mat.mtrl-sci↗

arXiv2025

Segregation, ordering, and precipitation in WTaV-based concentrated refractory alloys

Tungsten-based low-activation high-entropy alloys are possible candidates for next-generation fusion reactors due to their exceptional tolerance to irradiation, thermal loads, and stress. We develop an accurate and efficient machine-learned interatomic potential for the W-Ta-Cr-V system and use it in hybrid Monte Carlo molecular dynamics simulations of ordering and segregation to all common types of defects in WTaCrV. The predictions are compared to atom probe tomography analysis of segregation and precipitation in WTaCrV thin films. By also considering two other alloys, WTaV and MoNbTaVW, we are able to draw general conclusions about preferred segregation in refractory alloys and the reasons behind it, guiding future alloy design and elucidating experimental observations. We show that the experimentally observed CrV precipitates in WTaCrV form semicoherent bcc-to-bcc interfaces with the surrounding matrix, as coherent precipitates are not thermodynamically stable due to excessive lattice mismatch. The predictions from simulations align well with our atom probe tomography analysis as well as previous experimental observations.

cond-mat.mtrl-sci↗

arXiv2025

Prediction of defect properties in concentrated solid solutions using a Langmuir-like model

The alleged existence of sluggish diffusion in high entropy alloys has drawn controversy. In high entropy alloys, and in general in all solids, transport properties are controlled by point defect concentration, which must be known before performing atomistic simulations to compute transport coefficients. In this work, we present a general Langmuir-like model for defect concentration in an arbitrarily complex solid solution and apply this model to generate expressions for concentrations of vacancies and small interstitial atoms. We then calculate the vacancy concentration as a function of temperature in the equiatomic CoNiCrFeMn and FeAl alloys with modified embedded-atom-method potentials for various chemical orderings, showing there is no clear correlation between vacancy thermodynamics and chemical ordering in the CoNiCrFeMn alloy but clear systematic patterns for FeAl. We believe this is due to the high stability of disordered, random and ordered, intermetallic phases respectively in the CoNiCrFeMn and FeAl systems. This work provides future avenues to the prediction of thermal interstitials and vacancies in solid solutions, which is necessary for models of non-equilibrium behavior of solid solutions.

cond-mat.mtrl-sci↗

arXiv2024

Design Kinetic Parameters for Improved Resilience of Materials under Irradiation

High entropy alloys (HEAs) have captured much attention in recent years due to their conceivably improved radiation resistance compared to pure metals and traditional alloys. However, among HEAs, there are millions of design possibilities considering all potential compositions. In this study, we develop criteria to design HEAs with improved radiation resilience taking into consideration defect properties to promote interstitial-vacancy recombination. First, we conduct rate theory calculations on defects followed by molecular dynamics (MD) simulations on pure W and W-based multicomponent concentrated alloys. It is found that when the diffusion coefficients for single vacancies and interstitials become similar and the effective migration energies of defects is minimum (maximum diffusivities), defect recombination becomes optimal, and the concentration of defects is significantly reduced. This is supported by MD simulations indicating improved radiation resistance of V- and Cr-based alloys, which satisfy the above-stated criteria. Furthermore, experimental observations also reinforce the proposed approach. This study sheds light on the design criteria for improved radiation resistance and helps material selection without the need of extensive experimental work.

cond-mat.mtrl-sci↗

arXiv2024

Grain boundary metastability controls irradiation resistance in nanocrystalline metals

Grain boundaries (GBs) in polycrystalline materials are powerful sinks for irradiation defects. While standard theories assume that the sink efficiency of a grain boundary is defined solely by its character before irradiation, recent evidence conclusively shows that the irradiation sink efficiency is a highly dynamic property controlled by the intrinsic metastability of GBs under far-from-equilibrium irradiation conditions. In this paper, we reveal that the denuded (i.e., defect-free) zone, typically the signature of a strong sink, can collapse as irradiation damage accumulates. We propose a radiation damage evolution model that captures this behavior based on the emergence of a series of irradiation defect-enabled metastable GB microstate changes that dynamically alter the ability of the GB to absorb further damage. We show that these microstate changes control further defect absorption and give rise to the formation of a defect network that manifests itself as a net Nye-tensor signal detectable via lattice curvature experiments.

cond-mat.mtrl-sci↗

arXiv2024

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

cond-mat.mtrl-sci↗

arXiv2024

From anti-Arrhenius to Arrhenius behavior in a dislocation-obstacle bypass: Atomistic Simulations and Theoretical Investigation

Dislocations are the primary carriers of plasticity in metallic material. Understanding the basic mechanisms for dislocation movement is paramount to predicting the material mechanical response. Relying on atomistic simulations, we observe a transition from non-Arrhenius to Arrhenius behavior in the rate for an edge dislocation to overcome the elastic interaction with a prismatic loop in tungsten. Beyond the critical resolved shear stress, the process shows a non-Arrhenius behavior at low temperatures. However, as the temperature increases, the activation entropy starts to dominate, leading to a traditional Arrhenius behavior. We have computed the activation entropy analytically along the minimum energy path following Schoeck's methods [1], which capture the cross-over between anti-Arrhenius and Arrhenius domains. Also, the Projected Average Force Integrator (PAFI) [2], another simulation method to compute free energies along an initial transition path, exhibits considerable concurrence with Schoeck's formalism. We conclude that entropic effects need to be considered to understand processes involving dislocations bypassing elastic barriers close to the critical resolved shear stress. More work needs to be performed to fully understand the discrepancies between Schoeck's and PAFI results compared to molecular dynamics.

cond-mat.mtrl-sci↗

arXiv2024

Stochastic Integration of the Cahn-Hilliard Phase Field Equations

In this work we develop a stochastic algorithm to integrate the Cahn-Hilliard equations. The algorithm is based on Gillespie's stochastic simulation algorithm, also known as kinetic Monte Carlo. The deterministic integration of the phase field equations leads to the closest minimum of free energy and does not overcome free energy barriers. However, in the nucleation and growth regime of the phase diagram, free energy barriers need to be thermally overcome for the system to phase separate to reach equilibrium. We show in this work that the proposed stochastic integration algorithm indeed allows the system to overcome free energy barriers. We discuss the results in terms of fluctuation distributions, grid sizes and efficiency.

cond-mat.stat-mech↗