The study authors focused on MPEA, special alloys composed of more than three elements (in roughly equal proportions) that exhibit remarkable mechanical properties. These materials are much stronger, tougher, and more resistant to heat and wear than conventional alloys.
They are already used in aircraft parts, surgical implants, and clean energy systems. However, the traditional method of creating MPEA involved testing numerous combinations of elements. This process is costly and time-consuming.
To overcome this challenge, Deshmukh and this team developed a smart, data-driven system that combines the power of machine learning and algorithms. They started by gathering a large dataset of existing MPEA.