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This work proposes a computationally efficient approach for estimating the magnetization of Fe0.7Ni0.3body-centered cubic (bcc) nanoparticles (NPs) at room temperature using machine-learning algorithms, in terms of the average magnetic moment per atom, ⟨μ⟩. The magnetization data of isolated NPs were generated using atomistic spin dynamics (ASD) simulations for various nanoparticle shapes (cubes, spheres, octahedra, cones, cylinders, ellipsoids, flakes, and pyramids, with or without nanovoids) and FeNi distributions (random, core-shell, onion, sandwich, and Janus with different boundary planes). More than 1600 NPs were created and split into training and testing sets (70%–30% split), with features including the number of Ni/Fe surface and core atoms, potential energy distributions, pair correlation functions, and coordination distributions. Several machine-learning algorithms, including Random Forest (RF), Elastic Net, Support Vector Regression (SVR), and Gradient Boosting Regression (CatBoost), were applied to predict the average magnetic moment per atom of these NPs. The best-performing models, CatBoost and RF, achieved R2scores of up to 0.86, demonstrating their accuracy in predicting NP magnetization. Feature analysis highlighted the significance of the interface between Fe and Ni clusters, Fe–Fe interactions, and the presence of Fe on the surface as critical contributors to overall magnetization. Random alloy spherical NPs without porosity exhibited the highest ⟨μ⟩ ∼ 1.6μBdue to reduced Ni–Ni interactions. Applying machine-learning methods significantly reduces computational time and memory requirements compared to traditional ASD simulations. This allows for rapid prediction of NPs with desired magnetic properties, making them suitable for various technological applications.

Hemodynamics and morphology are recognized as major factors in the rupture risk of cerebral aneurysms, and exploration of their relationship is necessary to establish a method that can be employed by clinicians to assess the likelihood of rupture. In this work, morphological analysis and computational fluid dynamics were carried out to examine a database of 58 lateral cerebral aneurysms (26 ruptured and 32 unruptured) distributed among 49 patients. Eight morphological and six hemodynamic parameters were calculated and evaluated for statistical significance. It was observed that size ratio (SR), systolic wall shear stress (SWSS), diastolic wall shear stress (DWSS) and relative residence time (RRT) were statistically significant. The SR, DWSS, SWSS, and RRT were employed in multivariate logistic regression, obtaining a combined morphological–hemodynamic model, a pure morphological model, and a pure hemodynamic model to evaluate the odds ratio for rupture risk. The combined model had the highest efficiency, but no distinctive difference existed in the predictive capacity of the three models.

Metallic glasses (MGs) have been long investigated in material science to understand the origin of their remarkable properties. With the help of computational simulations, researchers have delved into structure-property relationships, leading to a large number of reports. To quantify the available literature, we employed systematic review and bibliometric analysis on studies related to MGs and classical molecular dynamics simulations from 2000 to 2021. It was found that the total number of articles has increased remarkably, with China and the USA producing more than half of the reports. However, high-impact articles were mainly conducted in the latter. Collaboration networks revealed that top contributor authors are strongly connected with other researchers, which emphasizes the relevance of scientific cooperation. In regard to the evolution of research topics, according to article keywords, plastic behavior has been a recurrent subject since the early 2000s. Nevertheless, the traditional approach of studying monolithic MGs at the short-range order evolved to complex composites with characterizations at the medium-range order, including topics such as nanoglasses, amorphous/crystalline nanolaminates, rejuvenation, among others. As a whole, these findings provide researchers with an overview of past and current trends of research areas, as well as some of the leading authors, productivity statistics, and collaboration networks.

Morphological characterization and fluid dynamics simulations were carried out to classify the rupture status of 71 (36 unruptured, 35 ruptured) patient specific cerebral aneurysms using a machine learning approach together with statistical techniques. Eleven morphological and six hemodynamic parameters were evaluated individually and collectively for significance as rupture status predictors. The performance of each parameter was inspected using hypothesis testing, accuracy, confusion matrix, and the area under the receiver operating characteristic curve. Overall, the size ratio exhibited the best performance, followed by the diastolic wall shear stress, and systolic wall shear stress. The prediction capability of all 17 parameters together was evaluated using eight different machine learning algorithms. The logistic regression achieved the highest accuracy (0.75), whereas the random forest had the highest area under curve value among all the classifiers (0.82), surpassing the performance exhibited by the size ratio. Hence, we propose the random forest model as a tool that can help improve the rupture status prediction of cerebral aneurysms.