Characterization of Z cluster connectivity in CuZr metallic glasses.

CONTEXT: Previous studies have proposed that the backbone of metallic glasses consists mainly of high-centrosymmetric structures, particularly Z clusters, which are responsible for the strength of the glass matrix. However, exploring these networks involves medium-range order analysis, a topic still not fully understood in the literature. This study investigates the atomic connectivity of CuZr metallic glasses by analyzing Z clusters using complex networks to establish their relationship with the mechanical behavior. Our results reveal higher connectivity and larger network sizes in the sample exhibiting the most pronounced stress overshoot, while the opposite trend is observed in samples with less pronounced stress overshoot. Metrics, such as density and clustering coefficient, further validate the correlation between Z cluster connectivity and mechanical behavior. These findings underscore the critical role of Z cluster connectivity in understanding the mechanical response of metallic glasses. METHODS: Molecular dynamics simulations were conducted using the LAMMPS software. Atomic interactions in Cu 50 Zr 50 metallic glasses were modeled using the embedded atom method, and compression tests were performed to assess the mechanical response. Atomic connectivity was examined through complex network analysis based on Z clusters, utilizing the NetworkX library for the Python programming language. Within this framework, parameters such as the average coordination number, network size, and network density were calculated, revealing the relationship between the interpenetrating Z cluster structure and the mechanical response of the samples.

Nanoporous Amorphous Carbon with Exceptional Ultra-High Strength.

Nanoporous materials show a promising combination of mechanical properties in terms of their relative density; while there are numerous studies based on metallic nanoporous materials, here we focus on amorphous carbon with a bicontinuous nanoporous structure as an alternative to control the mechanical properties for the function of filament composition.Using atomistic simulations, we study the mechanical response of nanoporous amorphous carbon with 50% porosity, with sp3 content ranging from 10% to 50%. Our results show an unusually high strength between 10 and 20 GPa as a function of the %sp3 content. We present an analytical analysis derived from the Gibson-Ashby model for porous solids, and from the He and Thorpe theory for covalent solids to describe Young's modulus and yield strength scaling laws extremely well, revealing also that the high strength is mainly due to the presence of sp3 bonding. Alternatively, we also find two distinct fracture modes: for low %sp3 samples, we observe a ductile-type behavior, while high %sp3 leads to brittle-type behavior due to high high shear strain clusters driving the carbon bond breaking that finally promotes the filament fracture. All in all, nanoporous amorphous carbon with bicontinuous structure is presented as a lightweight material with a tunable elasto-plastic response in terms of porosity and sp3 bonding, resulting in a material with a broad range of possible combinations of mechanical properties.

Machine learning modeling for the prediction of plastic properties in metallic glasses.

Metallic glasses are one of the most interesting mechanical materials studied in the last years, but as amorphous solids, they differ strongly from their crystalline counterparts. This matter can be addressed with the development and application of predictive techniques capable to describe the plastic regime. Here, machine learning models were employed for the prediction of plastic properties in CuZr metallic glasses. To this aim, 100 different samples were subjected to tensile tests by means of molecular dynamics simulations. A total of 17 materials properties were calculated and explored using statistical analysis. Strong correlations were found for stoichiometry, temperature, structural, and elastic properties with plastic properties. Three regression models were employed for the prediction of six plastic properties. Linear and Ridge regressions delivered the better prediction capability, with coefficients of determination above [Formula: see text]80% for three plastic properties, whereas Lasso regression rendered lower performance, with coefficients of determination above [Formula: see text]60% for two plastic properties. Overall, our work shows that molecular dynamics simulations together with machine learning models can provide a framework for the prediction of plastic behavior of complex materials.

Research on metallic glasses at the atomic scale: a systematic review.

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.

Using Integrated City Data and Machine Learning to Identify and Intervene Early on Housing-Related Public Health Problems.

CONTEXT: Housing is more than a physical structure-it has a profound impact on health. Enforcing housing codes is a primary strategy for breaking the link between poor housing and poor health. OBJECTIVE: The objective of this study was to determine whether machine learning algorithms can identify properties with housing code violations at a higher rate than inspector-informed prioritization. We also show how city data can be used to describe the prevalence and location of housing-related health risks, which can inform public health policy and programs. SETTING: This study took place in Chelsea, Massachusetts, a demographically diverse, densely populated, low-income city near Boston. DESIGN: Using data from 1611 proactively inspected properties, representative of the city's housing stock, we developed machine learning models to predict the probability that a given property would have (1) any housing code violation, (2) a set of high-risk health violations, and (3) a specific violation with a high risk to health and safety (overcrowding). We generated predicted probabilities of each outcome for all residential properties in the city (N = 5989). RESULTS: Housing code violations were present in 54% of inspected properties, 85% of which were classified as high-risk health violations. We predict that if the city were to use integrated city data and machine learning to identify at-risk properties, it could achieve a 1.8-fold increase in the number of inspections that identify code violations as compared with current practices. CONCLUSION: Given the strong connection between housing and health, reducing public health risk at more properties-without the need for additional inspection resources-represents an opportunity for significant public health gains. Integrated city data and machine learning can be used to describe the prevalence and location of housing-related health problems and make housing code enforcement more efficient, effective, and equitable in responding to public health threats.

Cerebral aneurysm rupture status classification using statistical and machine learning methods.

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.

Tension-compression behavior in gold nanoparticle arrays: a molecular dynamics study.

The mechanical properties of Au nanoparticle arrays are studied by tensile and compressive deformation, using large-scale molecular dynamics simulations which include up to 16 million atoms. Our results show that mechanical response is dominated by nanoparticle size. For compression, strength versus particle size shows similar trends in strength than full-density nanocrystals. For diameters (d) below 10 nm there is an inverse Hall-Petch (HP) regime. Beyond a maximum at 10 nm, strength decreases following a HP d -1/2 dependence. In both regimes, interparticle sliding and dislocation activity play a role. The array with 10 nm nanoparticles showed the same mechanical properties than a polycrystalline bulk with the same grain size. This enhanced strength, for a material nearly 20% lighter, is attributed to the absence of grain boundary junctions, and to the array geometry, which leads to constant flow stress by means of densification, nanoparticle rotation, and dislocation activity. For tension, there is something akin to brittle fracture for large grain sizes, with NPs debonding perpendicular to the traction direction. The Johnson-Kendall-Roberts contact theory was successfully applied to describe the superlattice porosity, predicting also the array strength within 10% of molecular dynamics values. Although this study is focused on Au nanoparticles, our findings could be helpful in future studies of similar arrays with NPs of different kinds of materials.

Mechanical and Antimicrobial Polyethylene Composites with CaO Nanoparticles.

Low-density polyethylene composites containing different sizes of calcium oxide (CaO) nanoparticles were obtained by melt mixing. The CaO nanoparticles were synthesized by either the sol-gel or sonication methods, obtaining two different sizes: ca. 55 nm and 25 nm. These nanoparticles were used either as-synthesized or were modified organically on the surface with oleic acid (Mod-CaO), at concentrations of 3, 5, and 10 wt% in the polymer. The Mod-CaO nanoparticles of 25 nm can act as nucleating agents, increasing the polymer's crystallinity. The Young's Modulus increased with the Mod-CaO nanoparticles, rendering higher reinforcement effects with an increase as high as 36%. The reduction in Escherichia coli bacteria in the nanocomposites increased with the amount of CaO nanoparticles, the size reduction, and the surface modification. The highest antimicrobial behavior was found in the composites with a Mod-CaO of 25 nm, presenting a reduction of 99.99%. This strong antimicrobial effect can be associated with the release of the Ca2+ from the composites, as studied for the composite with 10 wt% nanoparticles. The ion release was dependent on the size of the nanoparticles and their surface modification. These findings show that CaO nanoparticles are an excellent alternative as an antimicrobial filler in polymer nanocomposites to be applied for food packaging or medical devices.