CAREER: Integrating Information Theory with Data-Driven Mechanics: Toward Predictive Modeling of Material Behavior far from Equilibrium

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This Faculty Early Career Development (CAREER) award will support fundamental research on the modeling of material behavior far from equilibrium, such as crystal plasticity in metallic systems, solid-solid phase transformations, or particle rearrangements in disordered media. In all these examples, the observable macroscale material behavior is affected by the underlying atomic or particle microstructure. The lack of understanding of the linkage between microstructure and macroscale mechanical response hinders high-fidelity predictive capacity critical to structural and industrial applications, e.g., in infrastructure, transportation, and manufacturing. To illustrate further, in manufacturing, it leads to economic losses and barriers to innovation in processing of particles, forming of metals, and additive manufacturing. Progress aimed at closing this gap is therefore important and needed to advance the national health, prosperity, and welfare, as well as to secure the national defense. In addition, this grant will support an educational and outreach program to increase gender and racial equity in STEM fields, as well as to promote critical thinking and scientific literacy within the public.The long-term goal of this project is to realize a computational paradigm that delivers continuum macroscopic models of far-from-equilibrium mechanical behavior with atomic or particle fidelity. In pursuit of this goal, the objective is twofold: 1) integrate information theory practices with machine learning to identify precise macroscopic state variables that would represent coarse-grained multiscale processes starting from the atomic or discrete particle behavior, and 2) use state-of-the-art physics-based variational formulation to determine the continuum evolution equations for these variables. The approach will be validated using experimental rheological data for shearing of dense packings of colloidal particles, a scientifically rich and technologically relevant example of far-from-equilibrium mechanical behavior.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.