Composition driven machine learning for unearthing high-strength lightweight multi-principal element alloys

High-strength, lightweight alloys are highly desired in the aerospace, automotive and marine industries. The optimization of such alloys is a multifaceted process, characterized by the consideration of diverse objectives and constraints. Various optimization methodologies exist, spanning from intricate first-principle approaches to the application of sophisticated machine learning algorithms. These algorithms might incorporate input features encompassing elemental composition, microstructural attributes, and thermodynamic properties to enhance prediction accuracies. In this work, we aim to streamline this complexity by employing solely the alloy's elemental composition as the input feature for the machine learning algorithm, improving the hardness while reducing the density of the alloy. We have curated a comprehensive database comprising 544 multi-principal element alloys and developed a robust surrogate model based on these compositions. This composition-driven model is subsequently coupled with principal component analysis (PCA) to facilitate the selection process. Remarkably, through a mere three iterations involving 14 samples, we successfully identified an alloy with an effective specific hardness surpassing the training database maximum by 8.6 %. The proposed composition-driven machine learning delineates a simplified approach for conducting optimization across multiple target material properties.