IIT Guwahati, UK universities use machine learning to create sustainable metal alloys

Guwahati, Feb 4: Researchers from the Indian Institute of Technology (IIT) Guwahati, in collaboration with colleagues from the London South Bank University, the University of Manchester, and the University of Leeds, have developed a Machine Learning (ML)-based method to design advanced metal alloys that do not contain Critical Raw Materials (CRMs).

This innovation is expected to offer a practical route to identifying high-performance and sustainable materials that are not dependent on fragile global supply chains.

In recent years, a new class of materials, High-Entropy Alloys (HEAs), has attracted the attention of researchers and industry worldwide.

While traditional alloys contain small amounts of secondary metals in a primary metal, HEAs contain several metals in nearly equal amounts. These fall under the category of Multi-Principal Element Alloys (MPEAs).

HEAs are attractive because they offer many more combinations than traditional alloys and often exhibit excellent strength and stability at high temperatures.

Many high-performance HEAs used in areas such as aerospace engines, gas turbines, and nuclear power plants employ CRMs such as tantalum, niobium, tungsten, and hafnium. These elements are expensive, difficult to mine, and available in limited quantities.

Heavy reliance on such materials increases import dependence, strains supply chains, and adds environmental pressure due to mining. Reducing their use is, therefore, essential for sustainability and long-term industrial security.

To address this challenge, the research team led by IIT Guwahati developed a machine learning-assisted alloy design framework that focuses on identifying MPEAs that avoid the most critical raw materials.

The researchers first grouped CRMs into three levels based on supply risk, economic importance, and global availability. They created a database of 3,608 alloy compositions, focusing mainly on simple alloy systems built from elements that are not critically scarce.

The Extra Trees Regressor model was combined with different optimization techniques inspired by natural processes to search for alloy compositions that deliver high hardness without using CRMs.

A CRM-free alloy, Ti-Ni-Fe-Cu, was identified. The research team developed the newly proposed Ti–Ni–Fe–Cu alloy at a laboratory scale at IIT Kanpur and found its measured hardness to closely match the predicted value, confirming that the AI-based method works in practice.

The findings of the research have been published in ‘Scientific Reports’, a journal of the Nature Publishing Group, in a paper co-authored by Prof Joshi, along with his research team members Dr Swati Singh from IIT Guwahati, Prof Saurav Goel from the London South Bank University, Dr Mingwen Bai from the University of Leeds, and Prof Allan Matthews from the University of Manchester.