Novel nanomaterials with precisely tailored characteristics can enable innovation in areas ranging from manufacturing to energy storage and drug delivery but designing such materials can be a challenge. This project develops modeling and optimization techniques that will enable researchers to use desired properties to drive materials and process selection. The researcher harnesses the power of mesoscale modeling techniques and computational methods based on Bayesian machine learning and stochastic optimization to search the vast universe of options to identify promising candidates. This approach gives the community an enabling predictive tool for analysis and design of polymer-based nanomaterials. The knowledge gained from this research will be broadly disseminated through publications, conference presentations and by organizing symposia. Educational and outreach programs will be developed to train a diverse STEM workforce and to broaden participation of underrepresented students in the fields of engineering and computational science.
Integrating a mesoscale coarse-graining method with stochastic optimization provides an enabling tool in soft materials and advances knowledge about design exploration in high-dimensional search spaces and design optimization under uncertainty. The goal of this project is to significantly reduce the cost of simulating the molecular self-assembly process and the characteristics of assembled materials. The researcher will develop a mesoscale model that describes the dynamics of self-assembly and simulates and predicts the structures and mechanical properties of assembled materials. A coarse-grained approach balances the need for accuracy in material properties, which is the basis for optimization, and the computational efficiency needed to make the optimization feasible. The computational framework, which includes the mesoscale modeling, classification, and optimization steps, will be validated by comparison with experiments on peptoids. This research will enable inverse design of peptoid-based biomimetic nanomaterials with precisely tailored structures and properties for applications such as chemical/biological sensors, biomimetic nanodevices and water/ion transport membranes. The computational methodology will be shared through GitHub and as LAMMPS subroutines and the computer codes will be released to the scientific community as open-source software.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
