arXiv subjects

Jaime Marian

Publications and source records attributed to Jaime Marian.

At least 19 recordsLinked to original sources

arXiv2026

A computational alloy design framework for the promotion of amorphous grain boundary complexions

Amorphous grain boundary complexions have been shown to be radiation tolerant interfaces that can also reduce grain boundary embrittlement, marking them as favorable microstructural features. However, the incorporation of these features into new alloy systems is often a slow and arduous process based on trial and error. Here, a computational framework for alloy design is presented which enables the selection of dopants that promote the formation of amorphous grain boundary complexions. This framework is primarily built on density functional theory calculations and is demonstrated for W-rich binary and ternary alloys, which represent a promising target for fusion energy materials. Our framework first evaluates the grain boundary segregation tendency of dopants and then the energy penalty for amorphization alongside targeted interfacial energy comparison, with the end goal of identifying the best dopants. For a W base, Y and some transition metals such as Co and Ni are found to significantly lower these energetic barriers. Electronic structure analysis, local lattice distortion, and charge density distributions are calculated and used to provide mechanistic explanations for these dopant selections. Finally, the framework is validated by comparing with experimental literature for W alloys and a refractory complex concentrated alloy, showing a strong correlation between our dopant selections and low sintering onset temperatures that have been attributed to activated sintering. As a whole, this work establishes a transferable pipeline for designing alloys with grain-boundary complexions across diverse alloy systems.

cond-mat.mtrl-sci↗

arXiv2026

Ductility and Brittle Fracture of Tungsten by Disconnection Pile-up on Twin Boundaries

Refractory body-centered cubic (BCC) metals and alloys are of extraordinary importance in modern technological and structural applications. However, their wider adoption in science and technology is severely restricted by low-temperature brittleness, quantified by an unacceptably high value of the brittle-to ductile transition temperature (DBTT). The DBTT of these alloys is known to depend strongly on the particular microstructure of the material following mechanisms that are not well understood. Here we apply cross-scale molecular dynamics (MD), a simulation approach that preserves full atomic resolution while capturing the collective evolution of dislocations, twins, and cracks in near-micron-scale volumes, to investigate ductility and fracture in single-crystal tungsten pillars as a function of initial defect microstructure, deformation conditions, and temperature. The simulations reveal a sequence of microscopic processes conducive to failure: dislocation starvation, nucleation and growth of twins, pinning of the twin boundaries at surface asperities, resulting in disconnection pile-ups that trigger crack nucleation and propagation at low macroscopic stresses along incoherent boundary segments. By resolving these processes within a single atomistic framework, our simulations connect defect-level dynamics to macroscopic fracture behavior and identify microstructural pathways capable of shifting the DBTT through targeted promotion or suppression of the underlying deformation mechanisms.

cond-mat.mtrl-sci↗

arXiv2026

Irradiation-Driven Recrystallization in Fusion-Grade Tungsten: A Mesoscale, Microstructure-Aware Model

Tungsten (W) is the leading candidate material for plasma-facing components in fusion reactors, yet its upper operational temperature is limited by premature grain growth and recrystallization processes. Irradiation adds further complications by generating defect clusters and transmutation products that alter both the driving forces and kinetics of grain boundary motion. In this work, we develop a physics-based, multiscale framework that couples crystal plasticity, stochastic cluster dynamics, and discrete grain boundary dynamics to model the co-evolution of plastic deformation, irradiation damage, and grain growth in fusion-grade tungsten polycrystals. The approach enables simulations on realistic microstructures with arbitrary grain size and misorientation distributions, without recourse to mean-field simplifications. The model captures (i) the spatial heterogeneity of dislocation density distribution during hot working; (ii) irradiation-induced defect accumulation under fusion conditions, and (iii) the buildup of chemical and elastic driving forces for grain boundary migration and microstructural evolution. Parametric studies demonstrate the dominant influence that temperature has on thermally activated grain-boundary mobility, a weaker dependence on prior strain, and a pronounced retardation of recrystallization by rhenium segregation arising from neutron transmutation. Under fusion energy irradiation conditions, our framework predicts a substantial reduction of the effective recrystallization temperature relative to unirradiated microstructures, while Re production restores and even elevates this limit. By providing quantitative projections of recrystallization kinetics and in-service recrystallization temperatures, this work establishes a predictive tool for assessing the lifetime and operational envelope of W-based plasma-facing materials under fusion conditions.

cond-mat.mtrl-sci↗

arXiv2026

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials

Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties. We introduce phonon fine-tuning (PFT), which directly supervises second-order force constants of materials by matching MLIP energy Hessians to DFT-computed force constants from finite displacement phonon calculations. To scale to large supercells, PFT stochastically samples Hessian columns and computes the loss with a single Hessian-vector product. We also use a simple co-training scheme to incorporate upstream data to mitigate catastrophic forgetting. On the MDR Phonon benchmark, PFT improves Nequix MP by 55% on average across phonon thermodynamic properties and achieves state-of-the-art accuracy among models trained on Materials Project trajectories. PFT also generalizes to improve properties beyond second-derivatives, improving thermal conductivity predictions that rely on third-order derivatives of the potential energy.

cond-mat.mtrl-sci↗

arXiv2025

Parameter-free prediction of irradiation defect structures in tungsten at room temperature using stochastic cluster dynamics

The foundations of irradiation damage theory were laid in the 1950s and 60s within the framework of chemical reaction kinetics. While helpful to analyze qualitative aspects of irradiation damage, the theory contained gaps that delayed its implementation and applicability as a predictive tool. The advent of computer simulations with atomistic resolution in the 80s and 90s revealed a series of mechanisms that have proved essential to understand key aspects of irradiation damage in crystalline solids. However, we still lack a comprehensive model that can connect atomic-level defect physics with experimental measurements of quantitative features of the irradiated microstructure. In this work, we present a mesoscale model that draws from our improved understanding of irradiation damage processes collected over the last few decades, bridging knowledge gained from our most sophisticated atomistic simulations with defect kinetics taking place over time scales many orders of magnitude larger than atomic interaction times. Importantly, the model contains no adjustable parameters and combines several essential pieces of irradiation damage physics: each playing an irreplaceable role in the context of the full model but of limited utility if considered in isolation. Crucially, we carry out a set of experiments carefully designed to isolate the key irradiation damage variables and facilitate validation. Using tungsten as a model material, we find exceptionally good agreement between our numerical predictions and experimental measurements of defect densities and defect cluster sizes.

cond-mat.mtrl-sci↗

arXiv2025

Optimization of Transferable Interatomic Potentials for Glasses toward Experimental Properties

The accuracy of molecular simulations is fundamentally limited by the interatomic potentials that govern atomic interactions. Traditional potential development, which relies heavily on ab initio calculations, frequently struggles to reproduce the experimentally observed properties that govern real material behavior. To address this challenge, we present a machine learning-driven, active-learning optimization framework for optimizing classical interatomic potentials to reproduce experimental properties. Our method, here showcased on soda-lime borosilicate glasses, targets both global (density) and local (boron coordination) structural features across a wide range of compositions. By combining a surrogate model with iterative active learning, the framework efficiently explores a five-dimensional parameter space using only 400 molecular dynamics simulations over 17 iterations, making it highly data-efficient and eliminating the need for extensive simulation campaigns. Two transferable parameter sets are identified, each demonstrating good agreement with experimental measurements, including glass density, fraction of four-fold boron, and X-ray structure factor. The framework effectively captures and manages inherent trade-offs between structural objectives and compositional regimes, providing insights into the coordination behavior of boron in complex glass networks. The resulting classical force fields are generalizable and do not require reparameterization for individual compositions. Altogether, this work offers a scalable and experimentally grounded approach for developing transferable interatomic potentials suitable for a broad range of materials, including multi-component glass systems, and beyond.

cond-mat.dis-nn↗

arXiv2025

Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling

The design of structural & functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sophisticated computational materials modeling frameworks that span a wide spectrum of length & time scales in the mesoscale between atomistic & continuum approaches. This is leading towards a systems-based design methodology that will replace traditional empirical approaches, embracing the principles of the Materials Genome Initiative. However, several gaps remain in this framework as it relates to advanced structural materials:(1) limited availability & access to high-fidelity experimental & computational datasets, (2) lack of co-design of experiments & simulation aimed at computational model validation,(3) lack of on-demand access to verified and validated codes for simulation and for experimental analyses, & (4) limited opportunities for workforce training and educational outreach. These shortcomings stifle major innovations in structural materials design. This paper describes plans for a community-driven research initiative that addresses current gaps based on best-practice recommendations of leaders in mesoscale modeling, experimentation & cyberinfrastructure obtained at an NSF-sponsored workshop dedicated to this topic. The proposal is to create a hub for Mesoscale Experimentation and Simulation co-Operation (hMESO)-that will (I) provide curation and sharing of models, data, & codes, (II) foster co-design of experiments for model validation with systematic uncertainty quantification, & (III) provide a platform for education & workforce development. It will engage experimental & computational experts in mesoscale mechanics and plasticity, along with mathematicians and computer scientists with expertise in algorithms, data science, machine learning, & large-scale cyberinfrastructure initiatives.

cond-mat.mtrl-sci↗

arXiv2024

Electronic structure prediction of medium and high entropy alloys across composition space

We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of sampled compositions and descriptors required to accurately predict fields like the electron density increases rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries and maintain approximately the same descriptor-vector size as alloy elements increase. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.

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

Grain Boundary Defect Production during Successive Displacement Cascades on a Tungsten Surface

The interaction of radiation defects with grain boundaries (GBs) governs damage tolerance in refractory materials for extreme environments. Tungsten (W), a leading plasma-facing material for fusion, will be subjected to coupled ion and neutron irradiation that degrades both surface and bulk properties. In this study, molecular dynamics (MD) simulations are employed to examine defect evolution under successive 1 keV displacement cascades at a W surface in nano-bicrystals containing Sigma3 and Sigma5 symmetric tilt GBs. The free surface biases interstitial accumulation toward surface planes, reducing bulk interstitial populations, while vacancy saturation is driven by cascade overlap. When cascades indirectly interact with GBs, defect accumulation becomes strongly dependent on GB character. The higher energy Sigma5 boundary acts as a more effective defect sink for interstitials relative to the coherent Sigma3 boundary. This behavior arises from its larger interstitial segregation energy and enhanced strain field, which promote trapping via thermal migration and focused collision sequences. The deeper trap states of the Sigma5 GB suppress interstitial emission and limit recovery, whereas the shallower traps and mobile crowdion configurations in Sigma3 GBs enable dynamic defect recombination. These results highlight the critical role of GB structure and grain size in controlling radiation damage evolution in tungsten with behavior that applies broadly to refractory BCC metals.

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↗

arXiv2022

Thermal super-jogs control high-temperature strength in Nb-Mo-Ta-W alloys

Refractory multi-element alloys (RMEA) with body-centered cubic (bcc) structure have been the object of much research over the last decade due to their high potential as candidate materials for high-temperature applications. Most of these alloys display a remarkable strength at high temperatures, which cannot be explained by the standard model of bcc plasticity dominated by thermally-activated screw dislocation motion. Recent research on Nb-Mo-Ta-W alloys points to a heightened role of edge dislocations on deformation, which is generally attributed to atomic-level chemical fluctuations in the material and their interactions with dislocation cores during slip. However, while this model accounts for a strengthening effect due to the chemical complexity of the alloy, it is not sufficient to explain its strength across the entire thermal range. Here we propose a new mechanism that captures the existing theories about enhanced lattice strengthening and a thermally-activated component that emanates directly from the chemical complexity of the RMEA. This compositional complexity results in unique vacancy formation energy distributions with tails that extend into negative energies, leading to spontaneous, i.e., athermal, vacancy formation at edge dislocation cores. These vacancies relax into atomic-sized super-jogs on the dislocation line, acting as extra pinning points that increase the activation stress of the dislocation. At the same time, these super-jogs can displace diffusively along the glide direction, relieving with their motion some of the extra stress, thus countering the hardening effect due to jog-pinning. The interplay between these two processes as a function of temperature confers an extra strength to edge dislocation at intermediate-to-high temperatures, in remarkable agreement with experimental measurements in Nb-Mo-Ta-W and across a number of different RMEA.

cond-mat.mtrl-sci↗

arXiv2019

Constriction Percolation Model for Coupled Diffusion-Reaction Corrosion of Zirconium in PWR

Percolation phenomena are pervasive in nature, ranging from capillary flow, crack propagation, ionic transport, fluid permeation, etc. Modeling percolation in highly-branched media requires the use of numerical solutions, as problems can quickly become intractable due to the number of pathways available. This becomes even more challenging in dynamic scenarios where the generation of pathways can quickly become a combinatorial problem. In this work, we develop a new constriction percolation paradigm, using cellular automata to predict the transport of oxygen through a stochastically cracked Zr oxide layer within a coupled diffusion-reaction framework. We simulate such branching trees by generating a series porosity-controlled media. Additionally, we develop an analytical criterion based on compressive yielding for bridging the transition state in corrosion regime, where the percolation threshold has been achieved. Our model extends Dijkstras shortest path method to constriction pathways and predicts the arrival rate of oxygen ions at the oxide interface. This is a critical parameter to predict oxide growth in the so-called post-transition regime, when bulk diffusion is no longer the rate-limiting phenomenon.

physics.chem-ph↗

arXiv2019

Monte Carlo modeling of low-energy electron-induced secondary electron emission yields in micro-architected boron nitride surfaces

Surface erosion and secondary electron emission (SEE) have been identified as the most critical life-limiting factors in channel walls of Hall-effect thrusters for space propulsion. Recent wall concepts based on micro-architected surfaces have been proposed to mitigate surface erosion and SEE. The idea behind these designs is to take advantage of very-high surface-to-volume ratios to reduce SEE and ion erosion by internal trapping and redeposition. This has resulted in renewed interest to study electron-electron processes in relevant thruster wall materials. In this work, we present calculations of SEE yields in micro-porous hexagonal BN surfaces using stochastic simulations of electron-material interactions in discretized surface geometries. Our model consists of two complementary parts. First we study SEE as a function of primary electron energy and incidence angle in flat surfaces using Monte Carlo simulations of electron multi-scattering processes. The results are then used to represent the response function of discrete surface elements to individual electron rays generated using a ray-tracing Monte Carlo model. We find that micro-porous surfaces result in SEE yield reductions of over 50% in the energy range experienced in Hall thrusters. This points to the suitability of these micro-architected surface concepts to mitigate SEE-related issues in compact electric propulsion devices.

physics.comp-ph↗

arXiv2018

Monte Carlo Raytracing Method for Calculating Secondary Electron Emission from Micro-Architected Surfaces

Secondary electron emission (SEE) from inner linings of plasma chambers in electric thrusters for space propulsion can have a disruptive effect on device performance and efficiency. SEE is typically calculated using elastic and inelastic electron scattering theory by way of Monte Carlo simulations of independent electron trajectories. However, in practice the method can only be applied for ideally smooth surfaces and thin films, not representative of real material surfaces. Recently, micro-architected surfaces with nanometric features have been proposed to mitigate SEE and ion-induced erosion in plasma-exposed thruster linings. In this paper, we propose an approach for calculating secondary electron yields from surfaces with arbitrarily-complex geometries using an extension of the \emph{ray tracing} Monte Carlo (RTMC) technique. We study nanofoam structures with varying porosities as representative micro-architected surfaces, and use RTMC to generate primary electron trajectories and track secondary electrons until their escape from the outer surface. Actual surfaces are represented as a discrete finite element meshes obtained from X-ray tomography images of tungsten nanofoams. At the local level, primary rays impinging into surface elements produce daughter rays of secondary electrons whose number, energies and angular characteristics are set by pre-calculated tables of SEE yields and energies from ideally-flat surfaces. We find that these micro-architected geometries can reduce SEE by up to 50\% with respect to flat surfaces depending on porosity and primary electron energy.

cond-mat.mtrl-sci↗

arXiv2018

Structures and transitions in bcc tungsten grain boundaries and their role in the absorption of point defects

We use atomistic simulations to investigate grain boundary (GB) phase transitions in el- emental body-centered cubic (bcc) metal tungsten. Motivated by recent modeling study of grain boundary phase transitions in [100] symmetric tilt boundaries in face-centered cu- bic (fcc) copper, we perform a systematic investigation of [100] and [110] symmetric tilt high-angle and low-angle boundaries in bcc tungsten. The structures of these boundaries have been investigated previously by atomistic simulations in several different bcc metals including tungsten using the the γ-surface method, which has limitations. In this work we use a recently developed computational tool based on the USPEX structure prediction code to perform an evolutionary grand canonical search of GB structure at 0 K. For high-angle [100] tilt boundaries the ground states generated by the evolutionary algorithm agree with the predictions of the γ-surface method. For the [110] tilt boundaries, the search predicts novel high-density low-energy grain boundary structures and multiple grain boundary phases within the entire misorientation range. Molecular dynamics simulation demonstrate that the new structures are more stable at high temperature. We observe first-order grain boundary phase transitions and investigate how the structural multiplicity affects the mechanisms of the point defect absorption. Specifically, we demonstrate a two-step nucleation process, when initially the point defects are absorbed through a formation of a metastable GB structure with higher density, followed by a transformation of this structure into a GB interstitial loop or a different GB phase.

cond-mat.mtrl-sci↗

arXiv2017

Calculation of secondary electron emission yields from low-energy electron deposition in tungsten surfaces

We present calculations of secondary electron emission (SEE) yields in tungsten as a function of primary electron energies between 50 eV and 1 keV and incidence angles between 0 and 90°. We conduct a review of the established Monte Carlo methods to simulate multiple electron scattering in solids and select the best suited to study SEE in high-Z metals. We generate secondary electron yield and emission energy functions of the incident energy and angle and fit them to bivariate fitting functions using symbolic regression. We compare the numerical results with experimental data, with good agreement found. Our calculations are the first step towards studying SEE in nanoarchitected surfaces for electric propulsion chamber walls.

cond-mat.mtrl-sci↗

arXiv2017

Direct prediction of the solute softening-to-hardening transition in W-Re alloys using stochastic simulations of screw dislocation motion

Interactions among dislocations and solute atoms are the basis of several important processes in metals plasticity. In body-centered cubic (bcc) metals and alloys, low-temperature plastic flow is controlled by screw dislocation glide, which is known to take place by the nucleation and sideward relaxation of kink pairs across two consecutive \emph{Peierls} valleys. In alloys, dislocations and solutes affect each other's kinetics via long-range stress field coupling and short-range inelastic interactions. It is known that in certain substitutional bcc alloys a transition from solute softening to solute hardening is observed at a critical concentration. In this paper, we develop a kinetic Monte Carlo model of screw dislocation glide and solute diffusion in substitutional W-Re alloys. We find that dislocation kinetics is governed by two competing mechanisms. At low solute concentrations, nucleation is enhanced by the softening of the Peierls stress, which overcomes the elastic repulsion of Re atoms on kinks. This trend is reversed at higher concentrations, resulting in a minimum in the flow stress that is concentration and temperature dependent. This minimum marks the transition from solute softening to hardening, which is found to be in reasonable agreement with experiments.

cond-mat.mtrl-sci↗