This research is about how to gain higher confidence and better understanding of atomistic processes via simulation with the lack of perfect knowledge about physical systems, and how to improve the robustness of simulation prediction. The approach of this research will be rigorous and efficient quantification of input uncertainty associated with simulation models and its effects. The investigation will include new mechanisms that perform molecular dynamics simulation with incomplete knowledge of the system's states, kinetic Monte Carlo simulation with imprecise reaction and transition rates, first-principles reliable kinetic Monte Carlo simulation under uncertainties, as well as model calibration and validation with the incorporation of model errors and measurement errors.
If successful, the benefits of this research will include an efficient computational approach to enable rational design of clean and safe nanoscale products and materials (e.g. drugs, catalysts, fuel cells) in an affordable way to improve people's daily lives, enhanced computational infrastructure across disciplines to facilitate simulation-based scientific discovery and scalable nanomaterials production, publicly available open-source software for research and as an education tool for engineers and scientists, as well as course modules for teachers and 8th grade students on nanoscale engineering.
