Multiscale Simulation of Chemically Reactive Materials - Renewal 01 - Renewal

Abstract

The goal of this project is the development and application of a practical hierarchy of reactivecoarse-grained (CG) methods for the treatment of materials. Development and integration of three rigorous CG methodologies will permit the study of chemical reactivity across multiple time and length scales, especially for polymers. The highest resolution method, coarse-grained directed simulation (CGDS), employs a CG model to bias atomistic QM/MM simulations of key subregions of a large macro-molecular system to behave as if embedded in the full supramolecular structure. CGDS will be developed into a computational approach through a machine learning front end to identify order parameters for biasing and will be immediately applied to the study of polymer degradation mechanisms. CGDS results will then influence sampling in an emerging method, DFT-QM/CG-MM, which rigorously embeds a QM region into a CG environment. Study of reactivity with DFT-QM/CG-MM will allow macromolecular morphological changes to influence rates of reaction, due to the much larger MM region available than in traditional QM/MM. DFT-QM/CG-MM will be further developed to include treatments of electrostatic effects to increase its practical utility as a component in this hierarchy. Reaction parameters obtained through the combination of CGDS and QM/CG-MM approaches will finally be used to parameterize reactive CG (RCG) models able to follow multiple reactions at the CG scale for large polymeric systems. The final outcome of this project will be to apply the full CG hierarchy to study polymer degradation, leading to a complete multiscale model which will track competing reaction pathways and their interplay under disparate environmental effects. This reactive CG hierarchy can be applied to understand and design more robust polymers of Navy and general DoD relevance.

Document Details

Document Type
DoD Grant Award
Publication Date
Apr 06, 2021
Source ID
N000142112157

Entities

People

  • Gregory A. Voth

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Chicago

Tags

Readers

  • Computational Fluid Dynamics (CFD)
  • Linear Algebra
  • Quantum Chemistry

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference