Shape Memory Alloys (SMAs) are a class of metallic alloys that undergo reversible and repeatable martensitic transformations (MT) upon applying stress, magnetic fields, and/or temperature changes. These transformations can enable a wide range of technologies, including compact solid-state actuators, solid-state refrigerators, thermal storage and management systems, and structures that are stable against wide temperature changes. Unfortunately, current alloy formulations (with relatively simple chemistries) have been found to have significant limitations in their performance that prevent their widespread deployment in transformative technologies. This has pushed the field towards exploring alloys with increasingly complex chemistries and with more than three or four constituents being present in significant amounts [i.e., multi-principal element multi-functional alloys (MPEMFAs)]. Navigating this vast chemical space is extremely challenging. 

To address this challenge, this project will develop a novel closed-loop materials design framework, which can integrate experiments, computational materials science models, and machine learning (ML) / artificial intelligence (AI) approaches, with customized interfaces connecting experiments, models, existing data, and more critically, researchers across disciplines. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project aims to result in an enhanced understanding of an important class of materials to enable a wide range of technologies. Participating students will be trained in interdisciplinary approaches to materials discovery in the spirit of the Materials Genome Initiative (MGI).