Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization.

High-entropy alloys (HEAs) have attracted considerable attention due to their exceptional properties and outstanding performance across various applications. However, the vast compositional space and complex high-dimensional atomic interactions pose significant challenges in uncovering fundamental physical principles and effectively guiding alloy design. Traditional experimental approaches, often reliant on trial-and-error methods, are time-consuming, cost-prohibitive, and inefficient.

Development and characterization of bifunctional conductive and magnetic scaffold based on polyvinyl alcohol/polypyrrole/magnetite composite for neural tissue engineering.

The incorporation of electroconductive and magnetic materials into scaffolds for tissue engineering has emerged as an innovative approach to enhance nerve tissue regeneration. In this study, the freeze-drying technique was used to fabricate a bifunctional 3D neural scaffold based on biodegradable polyvinyl alcohol (PVA), incorporating magnetite nanoparticles (FeONPs) and the conductive polymer polypyrrole (PPy). Microstructural and chemical analyses using field emission scanning electron microscopy/energy-dispersive spectrophotometer, x-ray diffraction, and Fourier transform infrared spectroscopy revealed scaffolds with a homogeneous structure, interconnected pores averaging 100 µm, and over 80% porosity, with magnetite evenly distributed in the PVA matrix.

Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters.

Among various methods for fabricating polymeric tissue engineering scaffolds, electrospinning stands out as a relatively simple technique widely utilized in research. Numerous studies have delved into understanding how electrospinning processing parameters and specific polymeric solutions affect the physical features of the resulting scaffolds. However, owing to the complexity of these interactions, no definitive approaches have emerged.

Performance Analysis of Anode-Supported Solid Oxide Fuel Cells: A Machine Learning Approach.

Prior to the long-term utilization of solid oxide fuel cell (SOFC), one of the most remarkable electrochemical energy conversion devices, a variety of difficult experimental validation procedures is required, so it would be time-consuming and steep to predict the applicability of these devices in the future. For numerous years, extensive efforts have been made to develop mathematical models to predict the effects of various characteristics of solid oxide fuel cells (SOFCs) components on their performance (e.g.