(385w) Predicting Sequence-Dependent Polymer Properties Via Multiscale Simulations

• Simulations of synthetic and bio-based polymers
• Multiscale and hybrid modeling techniques
• High-throughput simulation and optimization workflows
• Predictive modeling to guide experimental design
• Data science and automation for materials discovery

Abstract:

Polymer synthesis has become increasingly precise, enabling the design of sequence-controlled materials with finely tunable properties. Among these, polypeptoids (N-substituted glycines) are a promising class of biomimetic polymers due to their synthetic scalability and vast range of customizable sidechain chemistries. However, this chemical diversity presents significant modeling challenges: structural datasets are scarce, and atomistic simulations are too computationally intensive to explore the full design space. To address this, we require predictive, physics-based tools that can bridge multiple length and time scales.

In this work, I present a multiscale modeling framework for sequence-defined polypeptoids that integrates atomistic simulations, coarse-grained (CG) models, and field-theoretic methods. Atomistic simulations reveal how features such as hydrophobic patterning and backbone chirality influence local solvation and chain rigidity. These atomistic insights guide the development of a CG model using relative entropy optimization, reducing simulation time by more than 150-fold while preserving sequence-specific behavior. This computational efficiency enables rapid exploration of longer chains and assembly behavior across many sequences. I am now expanding this multi-scale modeling approach to a field-theoretic framework that can rapidly predict sequence-dependent solubility. These findings expand our understanding of sequence-dependent behavior in polymers across longer length scales and offer new strategies for using sequence to design materials with tailored solubility and performance.

Broadly, I’m interested in applying multiscale and hybrid computational techniques across both synthetic and bio-derived polymer systems to accelerate materials discovery. My work integrates molecular simulation, statistical methods, and experimental collaboration to develop predictive, design-ready tools for real-world material challenges.