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).

According to a report in The Assam Tribune, a new class of materials, High-Entropy Alloys (HEAs), has attracted the attention of researchers and industry worldwide in recent years.

HEAs contain several metals in nearly equal amounts that fall under the category of Multi-Principal Element Alloys (MPEAs).

HEAs offer more combinations than traditional alloys and often showcase 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. 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 optimisation 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.