Prof. Martha Grover
Chemical & Biomolecular Engineering
Georgia Institute of Technology
Atoms and molecules interact dynamically via local forces, and these interaction rules can be manipulated through macroscopic system inputs such as temperature, pressure, and electric field. Moreover, the nature of the individual molecules can be designed to achieve a desired macromolecular assembly of the entire system. Biological systems achieve great complexity and robustness via this bottom-up molecular self-assembly, although human-designed systems are usually manufactured with a top-down approach.The molecular structure of a material strongly impacts its mechanical, electrical, and optical properties, and ultimately the performance of the system in which it is incorporated. Often a perfectly ordered crystalline structure is desired, but defects reduce performance from this ideal case. In other systems the intended material structure is amorphous, but the details of the nanocrystalline ordering and molecular orientation strongly impact the material properties. A material may exist in its thermodynamic equilibrium structure, but often materials are locked into non-equilibrium meta-stable configurations during their processing. Even though the perfect crystalline state may be the thermodynamic equilibrium, the dynamics of nucleation and growth of crystalline domains during temperature annealing may create distinct domains that intersect at grain boundaries. Dislocations and vacancies may also be locked in during processing. Non-equilibrium structures vastly increase the space of possible structures, and these dynamics can be intentionally exploited to achieve novel properties via time-varying process inputs. Stochastic simulations provide a quantitative framework in which to predict the overall dynamic organization of millions of atoms, based on local pair-wise interactions between individual atoms or small molecules. The events included in these kinetic Monte Carlo simulations may be selected using first-principles calculations, experimental measurements, or a combination of both. Two case studies will be described in this presentation. The first example focuses on surface morphology evolution during thin film deposition. Here the stochastic simulations provide the starting point to derive reduced-order coarse-grained models, which are subsequently used in a dynamic optimization to control the deposition process. In the second case study, the design of the local interaction rules between particles is considered, such that a desired assembly can be achieved in minimum time. Markov chain theory is employed to bound the convergence rate of the assembly process.
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Martha Grover is an Associate Professor in the School of Chemical & Biomolecular Engineering at the Georgia Institute of Technology. She earned her BS from the University of Illinois, Urbana-Champaign, and her MS and PhD from the California Institute of Technology. She joined Georgia Tech as an Assistant Professor in 2002, and received an NSF CAREER award in 2004. Her research program is dedicated to understanding, modeling, and engineering the self-assembly of atoms and small molecules to create larger scale structures and complex functionality. Her approach draws on process systems engineering, combining modeling and experiments in applications including surface deposition, crystal growth, polymer reaction engineering, and colloidal assembly. She is a member of the NSF Center for Chemical Evolution, and an affiliate member of the NASA Center for Ribosomal Origins and Evolution.
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