Layered Computational Tool Infrastructure

Software tools developed will use the following layered computational infrastructure:

Nanoscale transport phenomena models. Models of nanoscale electrical, chemical, mechanical, optical transport phenomena. At this level, the focus will be on capturing the characteristic behavior of the phenomena. For example, formation of the Taylor cone; onset of the Taylor instability at its tip; and droplet formation in electrohydrodynamic discharge. In addition, an important nanoscale manufacturing process is the creation of nanopores in two-dimensional materials. For example, nanopores in graphene are widely used for a variety of applications such as water desalination, sensing and sequencing. One approach that is used to create nanopores in graphene and other 2D materials is ion bombardment. In our proposed research, we will develop hierarchical and multiscale simulation tools for nanopore formation in 2D materials. In addition to nanopore formation, these simulation tools will also help elucidate stability of the pores under various conditions.


Process models. Models that introduce the geometry and dimensions of the manufacturing tool and controllable parameters of the process and attempt to predict the process output. Process models will be developed for e-jet printing, superionic stamping, transfer printing, two-photon lithography, and laser-tweezer particle assembly.

Uncertainty quantification framework. In order to make robust predictions, process models will be embedded in a framework that relates the uncertainty (the parameters of a distribution characterizing a process output) to that of the model input. Manufacturing processes, especially at nanoscale, contain significant uncertainties and it is important to account for these uncertainties in understanding transport phenomena, self-assembly, nanopore formation, and the like. Data-driven simulation tools for uncertainty quantification in manufacturing processes will be developed. Specifically, experimental data will be used to develop stochastic models of key uncertainties and propagate these uncertainties through hierarchical and multiphysical models to understand the role of uncertainties in manufactured parts and products at nanoscale.


Application and empirical validation of process models. A framework for the sequential design of experiments will be developed so that as additional input parameters and material are explored experimentally, the results of these explorations can be directly factored into the uncertainty quantification framework of the models. To facilitate this, experimental facilities for each of the processes will be developed/maintained through the duration of this project.


Tools for multiscale transport phenomena. Fluids based nanoscale manufacturing processes rely on transport phenomena at various length scales. In our proposed research, the node will develop hierarchical and multiscale simulation tools to understand transport phenomena in manufacturing processes. Specifically, quantum, atomistic and continuum approaches will be combined to understand transport phenomena such as interfacial structure and dynamics, electrical double layers, and transport rates under pressure and electric fields.


Tools for nanoscale self-assembly. A widely used manufacturing process at nanoscale is self-assembly. Some popular examples include nanoparticle self-assembly and patterning surfaces with molecules. Classical continuum approaches tend to be inaccurate for simulation of self-assembly phenomena. All atom based atomistic simulations are very expensive and they are limited to self-assembly of very small systems. The node will develop coarse-grained potentials and coarse-grained atomistic simulations to simulate self-assembly processes at nanoscale.