arXiv subjects

Albert Linda

Publications and source records attributed to Albert Linda.

7 recordsLinked to original sources

arXiv2026

Oblate Spheroid Excitation Theory: A Unified, Lattice-Free Foundation for Plastic Deformation from Which Dislocations Emerge as Collective Excitations

Dislocation theory has underpinned crystal plasticity for a century, yet its lattice-dependent definition cannot describe plastic flow in grain boundaries, glasses, ceramics, or nanocrystals near the glass transition, where no periodic lattice exists. We propose the Oblate Spheroid Excitation Theory (OSET): the elementary carrier of plastic deformation, in any solid, is a shear-eigenstrained oblate spheroid, the oblate-spheroidal transformation zone (OSTZ), treated within Eshelby's inclusion theory. The OSTZ requires no lattice and has a finite, non-singular, intrinsically thermally activated energy and stress fields. Three results are proved: a single OSTZ produces a non-singular elastic dipole, not a dislocation's singular field; a co-planar chain of N OSTZs is mathematically identical to a Peierls-Nabarro dislocation, core width and Burgers vector fixed by OSTZ geometry; and a genuine dislocation nucleates only once the chain reaches a host-lattice-set critical length. Dislocations emerge as a collective, large-$N$ limit of OSET rather than an assumed entity, and the theoretical shear strength, Peierls stress, core energy, Frank-Read critical stress, and stacking-fault energy follow as derived, parameter-free quantities. OSET is validated against grain-boundary-sliding data, independent literature spanning metals, ceramics, and bulk metallic glasses, and a recent 41-system compilation, reproducing the fitted dilatational and shear eigenstrains to within 2% and 15%, respectively. Because classical dislocation theory emerges from OSET but OSET does not require dislocations, it provides, in our view, a more fundamental, broadly applicable foundation for plastic deformation across material classes.

cond-mat.mtrl-sci↗

arXiv2026

Physics Aware Representation Learning on Electronic Charge Density for Materials Property Prediction

The fundamental quantity governing the mechanical and thermodynamic properties of a crystalline solid is its electronic charge density. Yet, its direct use for the rapid prediction of materials properties remains challenging due to its high dimensionality. Here, we present a physics-informed deep learning framework that directly predicts mechanical and thermodynamic properties from the three-dimensional electronic charge density derived from density functional theory (DFT). The proposed approach first utilizes a three-dimensional convolutional autoencoder for unsupervised dimensionality reduction, compressing a high-resolution charge-density grid (128 x 128 x 128) into a compact latent representation (16 x 16 x 16 x 16) while preserving physically meaningful features, as confirmed by negligible reconstruction errors across diverse crystal systems. The compressed latent-space representation of charge density is then used by two different regression models for property prediction: Light Gradient Boosting Machine (LightGBM) and Attention-based 3D Convolutional Neural Networks (Att CNN), and their performance is compared. Combining composition-based descriptors (Material Agnostic Platform for Informatics and Exploration or MAGPIE) with electronic charge density data further improves the model accuracy. Using a dataset of about 6059 inorganic compounds spanning multiple crystal symmetries, the models achieve strong predictive performance for bulk modulus K (R2 = 0.94), Young's modulus E (R2 = 0.88), shear modulus G (R2 = 0.87), formation energy Eform (R2 = 0.96), and Debye temperature Θ (R2 = 0.89). This work establishes electronic charge density as a transferable, physics-grounded descriptor for materials property prediction, requiring ~ 1/25 the computational resources of full-fledged DFT calculations.

cond-mat.mtrl-sci↗

arXiv2025

Multiscale Modeling of Abnormal Grain Growth: Role of Solute Segregation and Grain Boundary Character

Abnormal grain growth (AGG) influences the properties of polycrystalline materials; however, the underlying mechanisms, particularly the role of solute segregation at the grain boundary (GB), are difficult to quantify precisely. This study demonstrates a multiscale framework that integrates atomic-scale segregation energetics (using density functional theory) with mesoscale grain growth dynamics (using phase-field model) to investigate AGG, using $α$-Fe as an example system. Multisite segregation energies are calculated for symmetric tilt grain boundaries (STGBs) along the $\langle 110 \rangle$ axis for nine different solutes (Co, Cr, Mn, Mo, Nb, Ni, Ti, W, and V), encompassing three different types of coincident site lattice (CSL) boundaries: $\sum 3 (11\bar{2})$, $\sum 9 (\bar{2}21)$, and $\sum 3 (\bar{1}11)$. The model takes into account the effect of solute drag on GB mobility, estimated using a bulk solute concentration of 0.1 at\%. The results demonstrate that AGG originates due to GB anisotropy, the extent of which largely depends on the type of solute atom present. Such a complex dependence necessitates using a multiscale model to understand AGG comprehensively. In general, low-energy $Σ3$ boundaries are found to have higher mobility and show preferential growth for most of the solutes, other than Co. The study reveals how the distribution of GB types significantly influences AGG. When 10-30\% of the GBs are high-mobility type, crown-like morphologies are observed, leading to AGG. These findings underscore the critical role of GB chemistry and crystallography in governing AGG, and the model can be generalized to provide a predictive framework for controlling grain growth through strategic solute design in advanced alloys.

cond-mat.mtrl-sci↗

arXiv2025

Deep Learning Assisted Denoising of Experimental Micrographs

Microstructure imaging is crucial in materials science, but experimental images often introduce noise that obscures critical structural details. This study presents a novel deep learning approach for robust microstructure image denoising, combining phase-field simulations, Fourier transform techniques, and an attention-based neural network. The innovative framework addresses dataset limitations by synthetically generating training data by combining computational phase-field microstructures with experimental optical micrographs. The neural network architecture features an attention mechanism that dynamically focuses on important microstructural features while systematically eliminating noise types like scratches and surface imperfections. Testing on a FeMnNi alloy system demonstrated the model's exceptional performance across multiple magnifications. By successfully removing diverse noise patterns while maintaining grain boundary integrity, the research provides a generalizable deep-learning framework for microstructure image enhancement with broad applicability in materials science.

cond-mat.mtrl-sci↗

arXiv2024

Effect of Cr Segregation on Grain Growth in Nanocrystalline α-Fe Alloy: A Multiscale Modelling Approach

We present a multiscale modelling framework that integrates density functional theory (DFT) with a phase-field model (PFM) to explore the intricate dynamics of grain growth in nanocrystalline α-Fe single-phase alloy in the presence of chromium (Cr) segregation. We begin our study by validating our simulation results for equilibrium segregation in stationary GB with Mclean isotherm. Polycrystal simulations featuring nanocrystalline grains at different temperatures reveal that the grain growth kinetics depends on the ratio of Cr diffusivity to intrinsic GB mobility. In the absence of segregation, the relationship between the square of average grain size (d 2 ) and time (t) demonstrates a linear correlation. We observe that the d 2 vs. t plot exhibits a consistent linear trend up to a threshold grain size, independent of Cr segregation at GB. However, when Cr is segregated at GB, a deviation from this linear trend with a decreasing slope is evident within the temperature range of 700K to 900K beyond the threshold size. This threshold grain size decreases with increasing temperature. Notably, at 1000K, the deviation from the linear trend is observed from the initial stages of grain growth with segregation, albeit with a linear trend exhibiting a smaller slope. We also present an analytical formulation based on Cahn solute drag theory to predict grain growth behaviour in the presence of solute segregation and our simulation results well aligned this analytical formulation.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning

Stacking fault energies (SFEs) are vital parameters for understanding the deformation mechanisms in metals and alloys, with prior knowledge of SFEs from ab initio calculations being crucial for alloy design. Machine learning (ML) algorithms employed in the present work demonstrate approximately 80 times acceleration in predicting generalized stacking fault energy (GSFE), which is otherwise computationally expensive to obtain directly from density functional theory (DFT) calculations, particularly for alloys. The features used to train the ML algorithms stem from the physics-based Friedel model, revealing a connection between the physics of d-electrons and the deformation behavior of transition metals and alloys. Predictions based on the ML model are consistent with experimental data. This model could aid in accelerating alloy design by offering a rapid method for screening materials based on stacking fault energies.

cond-mat.mtrl-sci↗

arXiv2023

$μ$2mech: a Software Package Combining Microstructure Modeling and Mechanical Property Prediction

We have developed a graphical user interface (GUI) based package $μ$2mech to perform phase-field simulation for predicting microstructure evolution. The package can take inputs from ab initio calculations and CALPHAD (Calculation of Phase Diagrams) tools for quantitative microstructure prediction. The package also provides a seamless connection to transfer output from the mesoscale phase field method to the microscale finite element analysis for mechanical property prediction. Such a multiscale simulation package can facilitate microstructure-property correlation, one of the cornerstones in accelerated materials development within the integrated computational materials engineering (ICME) framework.

cond-mat.mtrl-sci↗