Topic: Machine Learning Methods for Lightweight Materials Design and Applications

Using lightweight materials in automobiles and aircraft represents a promising solution to save energy and reduce CO₂ emissions. Typical lightweight materials include light metals (Mg, Al, Ti) and their alloys, lightweight steels and high entropy alloys, and composite materials. These materials possess high specific strength, enabling structural support with reduced weight. Today, advanced lightweight materials are being designed to meet multiple requirements, such as strength at service temperature, formability, toughness, corrosion resistance, thermal conductivity, damping capacity, production cost, etc. The chemical composition and processing route for modern lightweight materials are becoming increasingly complicated, rendering the trial-and-error approach inefficient for new material development. Over the past decade, machine learning (ML) has been recognized as a revolutionary technique for finding optimal material solutions. In this Special Issue, we invite the submission of the latest research papers on the design and application of lightweight materials using ML methods in combination with computational simulations and high-throughput experimental methods.

Machine learning, magnesium, aluminum, titanium, lightweight steels, lightweight high entropy alloys, composite materials

30 Jun 2024