Nearly three decades since the discovery of high entropy alloys (HEAs), it has greeted a
broad interest in the field of materials research as a better alternative to conventional alloy …

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) …

In recent time, high entropy alloys (HEAs) are widely used due to their wide design space
and remarkable properties allowing a vast range of property variations with myriads of …

Eutectic high entropy alloys (EHEAs) with six components (Fe, Co, Ni, Cr, V and Ta) have
been designed and developed via vacuum arc melting route, following the guidance of the …

In the current research, machine learning (ML) models were used as a tool for predicting the
bulk modulus of High Entropy Alloys (HEAs). ML was employed to optimize HEA …

In contrast to conventional alloys, multicomponent high-entropy alloys (HEAs) have
emerged as promising candidates in the field of advanced materials because of their unique …

Eutectic high entropy alloys (EHEAs) with seven components (Fe, Co, Ni, Cr, V, Al, and Ta)
were designed and developed via vacuum arc melting route guided by the predictions …

The application of classification-based machine-learning techniques offers a faster
approach to designing high entropy alloys (HEAs). In this study, we have established a …

In this work, we have attempted to predict the mechanical behaviour of light weight Mg
based rare earth alloys fabricated through different mechanical and thermal processes. Our …