DMREF: Physics-Informed Meta-Learning for Design of Complex Materials

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

A wide class of high-performance materials, including solid-solid composites, porous solids, foams, biological materials, and additively manufactured materials, have complex microstructures, which play a dominant role in determining their properties and performance. This multidisciplinary project will harness recent innovations in artificial intelligence (AI) to establish a novel design and discovery cycle for complex materials that will dramatically accelerate material innovations. This project will create new methodologies through which human materials scientists and AI will collaborate to discover optimal microstructural designs of such complex materials for targeted properties and performance. There are enormous opportunities and needs for innovating next-generation materials through performance-driven design and optimization of microstructures. The AI-driven design framework in this Designing Materials to Revolutionize and Engineer our Future (DMREF) project will pioneer these opportunities through fundamental breakthroughs in AI for the design and machine learning of complex physical processes and will have a high impact on the materials research community. The success of this project will lead to an AI-driven material microstructure design framework, resulting in significant speedup in the discovery process of complex materials, as well as reducing the cost and labor required for material innovation by saving unnecessary “cut-and-try” experiments. The AI-driven design framework will be easily scalable and applicable to a broad range of complex materials, which will benefit the design and manufacturing of functional materials, polymers, composites, biomaterials, etc. By providing an accelerated discovery cycle and reduced costs, the design framework will benefit the US industry and, thereby, contribute to the safety, national security, and technological advancement of society. As such, this project will significantly accelerate and advance the discovery and development of materials with desirable properties and functionality, which aligns with the vision of the DMREF program. This project will benefit from collaboration with the Air Force Research Laboratory (AFRL) with respect to the manufacturing process of energetic materials and the testing and validation of design outputs of the AI framework against experimental results. Student training and workforce development will be enhanced through opportunities provided through AFRL. As the archetype of a complex material with strong microstructural influence on performance, this project will focus on energetic materials (EM), which cover the wide swathe of propellants, pyrotechnics, and explosives—key components in a variety of propulsion and munition systems critical to the US Military, as well as to civilian applications (construction, transportation, mining, etc.). This project will build new methods and tools to close the loop for AI-driven design of EMs, guiding the overall process of characterization, design/optimization, fabrication, experimentation, and validation through advanced machine cognition and game-theoretical decision making. To accomplish this goal, the investigators will first construct the space of a wide range of CHNO EMs and assemble machine learning datasets. The investigators will then develop a novel physics-informed meta-learning (PIML) framework for complex materials such as EMs, which will then be validated with experimental data and real uses cases. While achieving these, this project will make fundamental, use-inspired breakthroughs in AI-related to topics such as small data learning, weakly-supervised learning, and explainable AI, serving both the materials and AI communities. This project will benefit from collaboration with the Air Force Research Laboratory (AFRL) with respect to the manufacturing process of energetic materials and the testing and validation of design outputs of the AI framework against experimental results. Student training and workforce development will be enhanced through opportunities provided through AFRL. This project is jointly funded by NSF’s the Mathematical and Physical Sciences (MPS) Division of Materials Research (DMR) Designing Materials to Revolutionize and Engineer our Future (DMREF) program, the Established Program to Stimulate Competitive Research (EPSCoR), the Engineering (ENG) division of Civil, Mechanical, and Manufacturing Innovation (CMMI), and the Computer and Information Science and Engineering (CISE) division of Information and Intelligent Systems (IIS). 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.