Increasingly powerful computational resources and new algorithms have extended the application horizon of multiscale modeling, in which atomic scale phenomena are linked to macroscopic processing conditions. The aim of this project is to develop a hierarchical, multiscale-modeling framework suitable for use in repeated execution environments, such as process optimization and feedback control. This objective is becoming increasingly relevant as tolerances with respect to fluctuations in the spatial distribution of atomic species become tighter during the processing of advanced materials. Two classes of microstructural evolution will be considered in this project: (1) atomic aggregation in bulk crystalline semiconductor materials, including silicon, germanium, and silicon-germanium, and (2) species redistribution/segregation and phase separation in multilayered metallic alloys, such as copper-nickel, which are model systems for magnetic storage media.
Intellectual Merit: The project has both synthetic and integrative elements. Several contemporary multiscale modeling challenges will be addressed in order to construct a highly adaptive, coarse-grained lattice kinetic Monte Carlo simulation framework. The first aim will be to develop novel lattice kinetic Monte Carlo (LKMC) simulations that implicitly account for complex off-lattice rearrangements in real systems, particularly at the elevated temperatures common in semiconductor and metals processing. This will be accomplished by systematic comparison to data generated by large-scale equilibrium and non-equilibrium molecular dynamics simulations. The resulting "MD matched" LKMC simulations will then be implemented within recently introduced coarse-graining strategies that allow for systematic order-reduction with controlled error. A key outcome of this work will be strategies for applying this coarse-graining framework to LKMC simulations with complex interactions between multiple species. The coarse-graining will be implemented within a fully adaptive framework in which the degree of coarse-graining is adjusted dynamically in space and time as dictated by the evolving microstructure of the system as well as the resolution needs of the overall control/optimization environment. Many of the phenomena under consideration have been studied experimentally, and in the case of silicon, an ongoing collaboration with industry provides access to detailed microscopic data related to aggregate morphology as a function of thermochemical processing environment.
Broader Impacts: The material systems and microstructural evolution phenomena considered in this project are of fundamental interest in their own right but also are prototypical examples of a broad class of problems. Nucleation and growth of atomic clusters, and diffusion in spatially heterogeneous environments are cornerstone phenomena in a large number of processes related to the fabrication of advanced devices and materials. The project will bring together several aspects of multiscale modeling and integrate them into a control environment with the aim of developing a prototypical framework for multiscale optimization and control. The model developments will be applicable to a wide variety of processes and materials. For example, the adaptive coarse-grained LKMC method is an extremely powerful general approach that is multiscale without being highly system specific. The project will bring together elements from two traditionally different research areas and couple them closely. Large-scale molecular dynamics and basic kinetic Monte Carlo codes are already in place, allowing the graduate student working on this project to focus on novel aspects such as adaptive coarse-graining and integration of the optimization and control components with the multiscale models. The basic ideas of the project will be used to develop educational materials related to the Chemical and Biomolecular Engineering (CBE) senior design course at Penn. The development of new design projects that go beyond traditional chemicals processing into this course by the PI has been highly successful thus far. For example, recent projects have involved the design of chemical vapor deposition processes using finite element modeling, but no attempt has yet been made to include atomic-scale modeling. This work should provide a basis for creating design modules based on the optimization of microscopic objective functions and would represent yet another significant step in the evolution of the CBE capstone course.
