Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Materials Science & Engineering

First Advisor

Vivek B. Shenoy


Crucial thrusts in modern technology from electronic information processing to engineering cellular systems require manipulation and control of materials on smaller and smaller scales to succeed. A simple and successful way to break conventional material property limitations or design multifunctional devices is to interface two different materials together. At small length scales, the surface to bulk ratio of each component material increases, to the point that the interfacial physics can dominate the properties of the engineered system. Simultaneously, the combinatorial space of possible interfaces between materials and/or molecules is far too vast to explore by trial-and-error experimentation alone. Intuitive theoretical models can greatly improve our ability to navigate such large search spaces by providing insight on how two materials are likely to interact. The goal of this thesis is to develop predictive physical models which explain emergent phenomena at material interfaces across multiple length and time scales. A variety of state-of-the-art tools were applied to realize this goal, including analytical mathematics, quantum mechanical simulations, finite element methods, and deep neural networks. At the electron scale, a continuum model parametrized by first-principles simulations was employed to develop design criteria for confined quantum states in lateral heterostructures of two-dimensional materials. At the atomic scale, a chemo-mechanical model incorporating long-range electrostatics was developed to explain synthesizability trends in composite heterostructures of inorganic perovskites and organic molecules. A machine learning graph neural network model was developed and applied to predict the impact of general surface strains on the adsorption energy of small molecule intermediates on catalyst surfaces. Finally, at the microscale, a nonlinear kinetic model was developed to explain how cells acquire and retain memory of the mechanical properties of their surroundings across multiple timescales, which can lead to irreversible adaptation and differentiation. The methods and results presented in this thesis can improve our understanding of physical phenomena arising at interfaces and provide a blueprint for future applications of multiscale computational modeling to science and engineering problems.

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