arXiv subjects

Santiago Benito

Publications and source records attributed to Santiago Benito.

4 recordsLinked to original sources

arXiv2026

Iterative Thermodynamic Augmentation of Spatially Resolved Analytic Microscopy for Fast-Diffusing Solutes

The spatially resolved quantification of fast-diffusing solutes presents several challenges in analytic microscopy. Given the critical role of interstitially alloyed elements in physical metallurgy, we propose a computational framework that addresses this limitation by augmenting spatially resolved composition maps of substitutional elements with computationally derived interstitial distributions. The underlying methodology is an iterative thermodynamic model: exploiting the stark differences in solid-state diffusion kinetics, the model assumes a state of partial chemical equilibrium exclusively for the mobile interstitial species. An optimization scheme iteratively adjusts an uniform interstitial chemical potential across the mapped microstructure until the integrated local concentration converges with an independently measured bulk value. Ultimately, this approach extracts thermodynamically consistent interstitial concentration maps from robust, low-noise microscopy data, yielding quantitative spatial arrays that are otherwise time- and resource-intensive to obtain at best.

cond-mat.mtrl-sci↗

arXiv2026

Generative reconstruction of 2D and 3D polycrystalline microstructures using symmetrized hyperspherical harmonics

Establishing structure-property linkages in polycrystalline materials requires representative two- (2D) and three- (3D) dimensional microstructural inputs for full-field simulations. A core objective of microstructure characterization and reconstruction is the generative synthesis of 2D and 3D microstructures that reflect a target statistical ensemble using limited 2D data as a reference. This work introduces an orientation-based differentiable microstructure characterization and reconstruction framework, implemented in MCRpy, to perform reconstructions of voxelized images. Unit quaternions in combination with symmetrized hyperspherical harmonics are utilized to derive a continuous, symmetry-invariant representation of crystallographic orientations to overcome the numerical singularities and discontinuities associated with traditional Euler-based methods. The descriptor-based reconstructions are driven by a set combining two-point spatial correlations, a novel hybrid three-point variogram, and a mean variation regularizer to capture both global texture and local interfacial topology. The framework's efficiency is demonstrated by reconstructing 3D realizations from 2D orientation data of an aluminum alloy after thermo-mechanical processing, successfully recovering both morphological features and crystallographic distribution. Systematic benchmarking indicates that second-order gradient-based optimization, utilizing the L-BFGS-B algorithm, effectively navigates the complex loss landscape to generate high-fidelity realizations with minimal residuals. This methodology provides a versatile, open-source framework for the digital synthesis of polycrystalline representative volume elements to facilitate the rapid development of microstructure-informed materials design workflows.

cond-mat.mtrl-sci↗

arXiv2026

Impact of hydrogen incorporation on electronic and magnetic structure of X2CrNi18-9 stainless steel

Hydrogen absorption significantly alters the mechanical properties of steel. However, absorbed hydrogen also influences its electronic and magneto-structural properties, helping to interpret how hydrogen is incorporated. This study therefore investigates the influence of hydrogen incorporation on the electronic and magneto-structural properties of X2CrNi18-9 stainless steel in different microstructural states. Microstructural characterization included analytic electron microscopy mapping, X-ray diffraction and thermodynamic stability maps to evaluate grain size, dislocation density and chemical homogeneity. The electronic properties were characterized using the Seebeck coefficient, while the magneto-structural properties were investigated using diffuse neutron scattering and small-angle neutron scattering (SANS). Hydrogen incorporation showed clear changes in the Seebeck coefficients. Magnetic SANS in conjunction with diffuse neutron scattering indicates the existence of nanoscale inhomogeneities with the same fcc structure as the bulk, but with correlation lengths of a few nanometres. The size of these inhomogeneities increased with hydrogen incorporation, suggesting that hydrogen preferentially accumulates in their vicinity. However, no direct correlation between the electronic and magneto-structural properties and the dislocation density could be demonstrated. We suggest that studies such as these will lead in the medium term to the development of guidelines for material design to make steels more resistant to hydrogen.

cond-mat.mtrl-sci↗

arXiv2026

Multi-modal data-driven microstructure characterization

Electron backscatter diffraction is one of the most prevalent techniques used for microstructural characterization. In recent years, there has been an increase in the use of data-driven methods to analyze raw Kikuchi patterns. However, most of these require user input and the interpretation of the data-derived features is often challenging and subject to \textit{informed interpretation}. By using a combination of principal component analysis, constrained non-negative matrix factorization, and a variational autoencoder along with information-theoretical considerations on a multimodal dataset, it is shown that a) automated decision on method-specific hyperparameters, here the number of components in principal component analysis, the number of components for constrained non-negative matrix factorization, and the selection of reference constraints; and b) latent space features can be mapped to physically-meaningful quantities. In addition, the recommended region-of-interest (ROI) size for optimal model performance is approximated automatically to be twice the characteristic grain size based on information content of the dataset. Implemented in a workflow, this allows for a transferable, dataset-specific autonomous data-driven phase and grain segmentation including grain boundary detection and the analysis of very-small-angle intra-grain variations to complement conventional electron backscatter analysis.

cond-mat.mtrl-sci↗