DMREF: AI-Guided Accelerated Discovery of Multi-Principal Element Multi-Functional Alloys

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

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). This project aims to discover MPEMFAs with extreme property combinations, such as ultra-high temperature martensitic transformations (MTs) with low hysteresis, stable reversible shape change under stress, superelasticity at temperatures significantly beyond state-of-the-art; extreme properties, such as Invar and Elinvar effects up to 800°C; or uniquely tailored properties, such as SMAs-as-phase-change-materials (PCMs) with high thermal conductivity and transformation enthalpy but also with widely different MT temperatures. To navigate this vast chemical space a new framework will be developed that: (i) employs novel physics-informed machine learning to efficiently identify the feasible regions amenable to optimization; (ii) fuses simulations and experiments to obtain efficient ML models; (iii) develops new Batch (parallel) Bayesian Optimization (BO) strategies to make globally optimal iterative experimental design; and (iv) is capable of simultaneously considering multiple objectives and constraints. The aim is to go beyond accelerated discovery, seeking to address questions about the underlying factors responsible for the multi-functional behavior in MPEMFAs. The generated metadata, together with the computation and ML models, open-access code, end-to-end workflows, as well as high quality databases, will provide a testbed for developing and validating ML/AI frameworks when learning complex systems under data scarcity, particularly in ML/AI-drive materials discovery. The project will leverage the recently established interdisciplinary graduate certificate on materials science, informatics and design, Data-Enabled Discovery and Design of Energy Materials (D3EM), to train the PhD students supported by this effort, contributing to the workforce development goals of the Materials Genome Initiative. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.