CAREER: Data-Driven Systematic Hierarchical Modeling

点击翻译按钮获取中文摘要

Jianing Li of the University of Vermont & State Agricultural College is jointly supported by the Chemical Theory, Models and Computational Methods program in the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR) to predict the properties of materials using computer simulation models. The question of how to correlate molecular structures to material properties has been central to the chemical sciences. Computer modeling has been invaluable to help answer this fundamental question, but it is still very difficult to predict the properties of larger structures. Current simulation methods often reach their limitations, since they are not able to emulate large systems for long enough times. Dr. Li is now taking advantage of the immense data from molecular simulations previously completed. She is inventing an efficient approach to automatically learn from existing data to build new molecular models. These models are able to decrease the difficulty of simulating the large amount of underlying molecular components. By connecting these models to predictions of material properties (like stability, shape, and size), Dr. Li is using simulation methods to screen natural and man-made polymers for desirable properties and to accelerate the discovery of new materials. The materials designed in this project will be biocompatible, bioactive nanomaterials for numerous applications in sensing, drug delivery, tissue engineering, etc. The project also provides educational opportunities for students at multiple stages of their career development, by training graduate students with an interdisciplinary focus, as well as by encouraging undergraduate students early on to experiment independently with molecular modeling. The educational activities will broaden STEM participation by providing new learning and research opportunities to undergraduate students. Travel awards will be established to encourage underrepresented students in Vermont to attend the Green Mountain Winter Camp alongside their mentors and peers. For future technological advances in soft materials (e.g. to create new programmable, biocompatible nanostructures formed by peptides, DNAs, and organic polymers) it is critical to understand complex self-assembly processes and to be able to accurately predict the resulting structures. Hierarchical modeling represents an invaluable tool to understand such processes, since it can examine and predict (often in greater detail than experiments) how self-assembly occurs at the relevant atomic, nanoscopic, and mesoscopic scales. However, to invent more powerful hierarchical computational methods for the future, universal highly coarse-grained (HCG) force fields in conjugation with effective backmapping methods are needed. To target these challenges, Dr. Li is creating a systematic, data-driven hierarchical (STAIR) methodology for multiscale modeling. STAIR is designed to overcome major drawbacks of currently available methods for systematic coarse graining, which still require substantial human expertise and labor for force field development. Specifically, STAIR replaces expensive fitting processes by innovative neural network algorithms and reduce human efforts in tasks like particle type determination, ad hoc corrections, etc. With the long-term goal to guide the rational design of complex nanomaterials from relatively simple building blocks, the overall objective is to invent hierarchical, adaptive modeling methods to guide the development of peptide- and DNA-based self-assembled nanomaterials.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.