From High-Entropy Alloys to Alloys with High Entropy: A New Paradigm in Materials Science and Engineering for Advancing Sustainable Metallurgy

TL;DR

This paper discusses a new paradigm in alloy design that utilizes high-entropy alloys with multiple elements, promoting sustainable metallurgy through advanced data-driven modeling and AI tools to explore diverse, eco-friendly alloy compositions.

Contribution

It introduces a novel alloy design framework emphasizing high-entropy alloys, integrating advanced modeling and AI to optimize sustainable alloy development from multi-component waste materials.

Findings

High-entropy alloys broaden alloy design options.

AI and modeling are crucial for exploring vast alloy compositions.

New alloys can be developed from scrap and electronic waste.

Abstract

The development of high-entropy alloys (HEAs) has marked a paradigm shift in alloy design, moving away from traditional methods that prioritize a dominant base metal enhanced by minor elements. HEAs instead incorporate multiple alloying elements with no single dominant component, broadening the scope of alloy design. This shift has led to the creation of diverse alloys with high entropy (AHEs) families, including high-entropy steels, superalloys, and intermetallics, each highlighting the need to consider additional factors such as stacking fault energy (SFE), lattice misfit, and anti-phase boundary energy (APBE) due to their significant influence on microstructure and performance. Leveraging multiple elements in alloying opens up promising possibilities for developing new alloys from multi-component scrap and electronic waste, reducing reliance on critical metals and emphasizing the need for advanced data generation techniques. With the vast possibilities offered by these multi-component feedstocks, modelling and Artificial Intelligence based tools are essential to efficiently explore and optimize new alloys, supporting sustainable progress in metallurgy. These advancements call for a reimagined alloy design framework, emphasizing robust data acquisition, alternative design parameters, and advanced computational tools over traditional composition-focused methodologies.