This award supports interdisciplinary computational research and education aimed to advance understanding and control of the microstructure of materials. Some important properties of materials, such as mechanical strength or corrosion susceptibility, are largely a function of microstructure: the sizes, shapes, and arrangement of material constituents, for example grains in polycrystals, over distances of micrometers. Despite the importance of microstructure, techniques for synthesizing and processing materials to obtain desired microstructures, and therefore desired materials properties, remain limited. Understanding how microstructure evolves in time as a material is made remains a fundamental challenge and the susceptibility of microstructure evolution to external control is nearly unexplored. This project investigates the fundamental physical characteristics that make microstructure evolution amenable to external control and explores novel pathways for creating designer microstructures through incorporation of materials modeling, real-time feedback, and control into material processing. This work will close a deep knowledge gap concerning the fundamental limits of microstructure controllability, thereby laying the foundation for the systematic control of microstructure evolution. "Controllability" itself will be a fundamental physical property emerging from the mechanisms that govern microstructure evolution. The computational tools and fundamental insights provided by this project will guide future development of microstructure controllers to be used in specific materials processing applications. The computational aspects of this project are extremely demanding and will require the full power of exascale computers: ones that perform 1 billion billion floating point operations per second. A major part of this project is therefore to efficiently parallelize nearly every stage of computation.
This award supports interdisciplinary computational research and education aimed to advance understanding and control of the microstructure of materials. While much effort has been invested into the design, discovery, and optimization of new materials, optimal control of materials processing is relatively less well understood. In particular, the fundamental physical characteristics of an evolving microstructure that determine the degree of susceptibility to external control remain unexplored. This project will lay the foundations for material microstructure control by: a) designing microstructure-sensitive feedback controllers, b) developing computational tools for efficient implementation of feedback control, and c) systematically exploring feedback controller performance with respect to metrics such as objective fidelity, energy-efficiency, and cost-optimality. This work lies at the intersection of control theory, materials modeling, and high-performance computing. Ultimately, the researchers aim to create optimal controllers for the synthesis of designer materials by exploiting massively parallel, high performance computing architectures to explore fundamental questions of controllability in materials processing, such as: How does one optimally control materials processing parameters to make designer microstructures? What are the limits to the types of microstructures that may be made using a given processing method? What are the minimal requirements for a processing method to be able to synthesize a given type of microstructure? This work will explore the fundamental limits of materials microstructure controllability using the tools of control theory: the branch of engineering that creates optimal algorithms for the autonomous operation of vehicles, aircraft, and robots. To enable rapid investigation of this new field, the project will use computational materials models as surrogates for physical materials. The goal is to develop model-agnostic control tools that are transferrable between different models. However, effort will initially focus on one specific model problem, namely: phase field models of microstructure evolution as governed by the Allen-Cahn equation. Since the control methods to be developed will be sufficiently general that they can be used with other materials models.
This award is jointly supported by the Condensed Matter and Materials Theory Program in the Division of Materials Research in the Directorate for Mathematical and Physical Sciences, the Civil, Mechanical and Manufacturing Innovation Division in the Engineering Directorate, and the Software and Hardware Foundations Program in the Division of Computing and Communications Foundations in the Directorate for Computer and Information Science and Engineering.
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.
