arXiv subjects

Laurent Capolungo

Publications and source records attributed to Laurent Capolungo.

10 recordsLinked to original sources

arXiv2026

Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors

Materials behavior is often treated as a deterministic mapping from structure to properties, yet many important phenomena emerge from the conditional activation of multiple mechanisms across scales. This is especially evident in fatigue of metals, where crack growth is typically modeled as monotonic and irreversible process, despite evidence that local microstructure, loading history, and competing unit processes can shift the balance among propagation, arrest, and self-healing. Here we present a probabilistic framework that describes materials behavior as an ensemble of constituent mechanisms whose activation, interaction, and evolution determine emergent outcomes. The framework connects mechanism activation, state evolution, and macroscopic observables in a probabilistic way. In the case of fatigue crack propagation, it reframes damage tolerance as an inference problem over mechanism competition and provides a basis for integrating multiscale simulation, multimodal characterization, and machine learning. The same logic extends to other physical and chemical systems suggesting a portable framework for any system in which emergent behavior reflects mechanism competition under changing conditions. The broader ambition of this perspective review is a shift from correlating structure and performance after the fact to identifying, in advance, the conditions that make desired emergent behavior probable.

cond-mat.mtrl-sci↗

arXiv2026

Insights into hydrogen-induced vacancy stability and creep in chemically complex alloys

Hydrogen (H) content modifies the creep response of Fe-based alloys by altering thermodynamics of point-defects; here we identify the electronic-structure mechanism underlying this effect. Using spin-polarized first-principles calculations combined with a cluster dynamics formulation, we establish a general framework linking H-assisted vacancy stabilization to diffusion-mediated creep in BCC Fe, FCC Fe, and chemically complex FCC Fe-Cr-Ni alloys. H-vacancy binding analysis shows that H-stabilized vacancies form at low hydrogen content in BCC Fe but require much higher chemical potentials in FCC Fe and Fe-Cr-Ni alloys due to broader d-bands, electronic screening, and chemical disorder. Consequently, plastic deformation mediated by diffusive processes is expected to be far more strongly impacted in BCC Fe than in FCC alloys. These electronic-controlled trends determine steady-state vacancy populations and provide a symmetry-resolved microscopic basis for H-assisted creep in ferritic and austenitic steels.

cond-mat.mtrl-sci↗

arXiv2026

Towards Spatio-Temporal Extrapolation of Phase-Field Simulations with Convolution-Only Neural Networks

Phase-field simulations of liquid metal dealloying (LMD) can capture complex microstructural evolutions but can be prohibitively expensive for large domains and long time horizons. In this paper, we introduce a fully convolutional, conditionally parameterized U-Net surrogate designed to extrapolate far beyond its training data in both space and time. The architecture integrates convolutional self-attention, physically informed padding, and a flood-fill corrector method to maintain accuracy under extreme extrapolation, while conditioning on simulation parameters allows for flexible time-step skipping and adaptation to varying alloy compositions. To remove the need for costly solver-based initialization, we couple the surrogate with a conditional diffusion model that generates synthetic, physically consistent initial conditions. We train our surrogate on simulations generated over small domain sizes and short time spans, but, by taking advantage of the convolutional nature of U-Nets, we are able to run and extrapolate surrogate simulations for longer time horizons than what would be achievable with classic numerical solvers. Across multiple alloy compositions, the framework is able to reproduce the LMD physics accurately. It predicts key quantities of interest and spatial statistics with relative errors typically below 5% in the training regime and under 15% during large-scale, long time-horizon extrapolations. Our framework can also deliver speed-ups of up to 36,000 times, bringing the time to run weeks-long simulations down to a few seconds. This work is a first stepping stone towards high-fidelity extrapolation in both space and time of phase-field simulation for LMD.

cs.CE↗

arXiv2025

Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures

Phase-field models of liquid metal dealloying (LMD) can resolve rich microstructural dynamics but become intractable for large domains or long time horizons. We present a conditionally parameterized, fully convolutional U-Net surrogate that generalizes far beyond its training window in both space and time. The design integrates convolutional self-attention and physics-aware padding, while parameter conditioning enables variable time-step skipping and adaptation to diverse alloy systems. Although trained only on short, small-scale simulations, the surrogate exploits the translational invariance of convolutions to extend predictions to much longer horizons than traditional solvers. It accurately reproduces key LMD physics, with relative errors typically under 5% within the training regime and below 10% when extrapolating to larger domains and later times. The method accelerates computations by up to 16,000 times, cutting weeks of simulation down to seconds, and marks an early step toward scalable, high-fidelity extrapolation of LMD phase-field models.

cs.CE↗

arXiv2025

A Comparison of Surrogate Constitutive Models for Viscoplastic Creep Simulation of HT-9 Steel

Mechanistic microstructure-informed constitutive models for the mechanical response of polycrystals are a cornerstone of computational materials science. However, as these models become increasingly more complex - often involving coupled differential equations describing the effect of specific deformation modes - their associated computational costs can become prohibitive, particularly in optimization or uncertainty quantification tasks that require numerous model evaluations. To address this challenge, surrogate constitutive models that balance accuracy and computational efficiency are highly desirable. Data-driven surrogate models, that learn the constitutive relation directly from data, have emerged as a promising solution. In this work, we develop two local surrogate models for the viscoplastic response of a steel: a piecewise response surface method and a mixture of experts model. These surrogates are designed to adapt to complex material behavior, which may vary with material parameters or operating conditions. The surrogate constitutive models are applied to creep simulations of HT-9 steel, an alloy of considerable interest to the nuclear energy sector due to its high tolerance to radiation damage, using training data generated from viscoplastic self-consistent (VPSC) simulations. We define a set of test metrics to numerically assess the accuracy of our surrogate models for predicting viscoplastic material behavior, and show that the mixture of experts model outperforms the piecewise response surface method in terms of accuracy.

physics.comp-ph↗

arXiv2025

Effect of Anisotropic Peierls Barrier on the Evolution of Discrete Dislocation Networks in Ni

Over low and intermediate strain rates, plasticity in face centered cubic (FCC) metals is governed by the glide of dislocations, which manifest as complex networks that evolve with strain. Considering the elastic anisotropy of FCC metals, the characteristics of dislocation motion are also anisotropic (i.e., dislocation character angle-dependent), which is expected to notably influence the overall evolution of the dislocation network, and consequently, the plastic response of these materials. The aggregate influence of the anisotropy in the Peierls stress on the mechanical response of single crystal Ni was investigated in the present work using discrete dislocation dynamics simulations. Twenty initial dislocation networks, differing in their configuration and dislocation density, were deformed under uniaxial tension up to at least 0.9% strain, and the analysis of character-dependent dynamics showed a suppression of plasticity only for segments of nearly screw character. While the increased screw component of the Peierls stress raised the initial strain hardening rate, it also resulted in longer dislocation segments overall, contrary to the reasoning that longer pinned segments exhibit a lower resistance to motion and might give a weaker response. A non-linear superposition principle is demonstrated to predict the hardening reasonably well, considering the cumulative effects of forest and Peierls stress-related strengthening. Further analysis of the network topology revealed a tendency to maintain connectivity over the course of deformation for those networks simulated using an unequal Peierls stress. The general increases in hardening rate and network connectivity contrast with the localized reduction of dislocation motion, which occurred mainly for segments of nearly screw-type character.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For such liquid-metal dealloying (LMD) process, phase field models have been developed. However, the governing equations often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, stiffness in the PDEs requires an extremely small time steps (e.g. $10^{-12}$ or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose U-Shaped Adaptive Fourier Neural Operators (U-AFNO), a machine learning (ML) model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g. the deformation of the liquid-metal interface) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in-spite of the chaotic nature of LMD. Our model reproduces the key micro-structure statistics and QoIs with a level of accuracy on-par with the high-fidelity numerical solver. We also investigate the opportunity of using hybrid simulations, in which we alternate forward leap in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

cs.CE↗

arXiv2023

Grain Boundary Effects in Dealloying Metals: A Multi-Phase Field Study

A multi-phase field model is employed to study the microstructural evolution of an alloy undergoing liquid dealloying. The model proposed extends upon the original approach of Geslin et al. to consider dealloying in the presence of grain boundaries. The model is implemented using a semi-implicit time stepping algorithm using spectral methods, which enables simulating large 2D and 3D domains over long time-scales while still maintaining a realistic interfacial thickness. The model is exercised to demonstrate a mechanism of coupled grain-boundary migration to maintain equilibrium contact angles with this topologically-complex solid-liquid interface during dealloying. This mechanism locally accelerates dealloying by dissolving the less noble alloy metal from (and rejecting the more noble metal into) the migrating grain boundary, thereby enhancing the diffusion-coupled-growth of the liquid channel into the precursor. The deeper corrosion channel at the migrating grain boundary asymmetrically disrupts the ligament connectivity of the final dealloyed structure, in qualitative agreement with published experimental observations. It is shown that these grain boundary migration-assisted corrosion channels form even for precursors with small amounts of the dissolving alloy species, below the so-called \textit{parting limit}

cond-mat.mtrl-sci↗

arXiv2022

Effect of a Micro-scale Dislocation Pileup on the Atomic-Scale Multi-variant Phase Transformation and Twinning

In this paper, we perform concurrent atomistic-continuum (CAC) simulations to (i) characterize the internal stress induced by the microscale dislocation pileup at an atomically structured interface; (ii) decompose this stress into two parts, one of which is from the dislocations behind the pileup tip according to the Eshelby model and the other is from the dislocations at the pileup tip according to a super-dislocation model; and (iii) assess how such internal stresses contribute to the atomic-scale phase transformations (PTs), reverse PTs, and twinning. The main novelty of this work is to unify the atomistic description of the interface and the coarse-grained (CG) description of the lagging dislocations away from the interface within one single framework. Our major findings are: (a) the interface dynamically responds to a pileup by forming steps/ledges, the height of which is proportional to the number of dislocations arriving at the interface; (b) when the pre-sheared sample is compressed, a direct square-to-hexagonal PT occurs ahead of the pileup tip and eventually grows into a wedge shape; (c) upon a further increase of the loading, part of the newly formed hexagonal phase transforms back to the square phase. The square product phase resulting from this reverse PT forms a twin with respect to the initial square phase. All phase boundaries (PBs) and twin boundaries (TBs) are stationary and correspond to zero thermodynamic Eshelby driving forces; and (d) the stress intensity induced by a pileup consisting of 16 dislocations reduces the stress required for initiating a PT by a factor of 5.5, comparing with that in the sample containing no dislocations. This work is the first characterization of the behavior of PTs/twinning resulting from the reaction between a microscale dislocation slip and an atomically structured interface.

cond-mat.mes-hall↗

arXiv2013

On the importance of prismatic/basal interfaces in the growth of (-1012) twins in hexagonal close-packed crystals

The growth process of of (-1012) twins is studied in Magnesium using atomistic simulations. Two twin seeds are considered and both cases, a specific interface, which places face-to-face prismatic and basal planes, plays an important role. This interface has a low energy corresponding to a cusp in the orientation-dependent interface energy of a twinned bicrystal. This interface appears in several published twin structures and for instance accommodates the large deviations of twin interfaces from (-1012) planes reported recently [Zhang et al., Scr. Mater. 67 (2012) 862].

cond-mat.mtrl-sci↗