Compared to conventional alloys, multicomponent high-entropy alloys (HEAs) have
received considerable attention in recent years owing to their exceptional phase stability …

The exploration of high entropy alloys (HEAs) primarily relies on trial-and-error experiments
and multiscale modelling, which are time-consuming and resource-intensive. Recently …

Features calculation and combinatorial screening are necessary and tedious in predicting
the hardness of high-entropy alloys by empirical parameters. To simplify the prediction …

A machine learning technique leveraging artificial intelligence (AI) has emerged as a
promising tool for expediting the exploration and design of novel high entropy alloys (HEAs) …

An equiatomic CoCrFeMnNi high entropy alloy (HEA) was prepared by the gas atomization
process. In addition, high-energy milling was carried out to study the effects of milling …

Most conventional methods only consider the effects of materials and loading conditions
when predicting the high-temperature low-cycle fatigue life of titanium aluminum alloys …

This research article presents a comprehensive study on the prediction of thermal
conductivity (TC) as a primary outcome for an artificial neural network (ANN) model in the …

Membrane electrode assembly (MEA) is considered a key component of a proton exchange
membrane fuel cell (PEMFC). However, develo** a new MEA to meet desired properties …

In this work, the effect of metallurgical changes due to heat treatment on the properties of
high entropy alloys has been explored. Moreover, the effect of annealing temperature and …

Solid solution strengthening theory is essential for designing steel with high microhardness.
Experimental determination is quite time consuming and costly. It is necessary to develop an …