Laboratory Learning Program 2024 - Molecular Modeling and Machine Learning of Smart Polymeric Materials (CBE-04)

Research Opportunity Number: CBE-04

Project Title: Molecular Modeling and Machine Learning of Smart Polymeric Materials

Project Summary: Stimuli-responsive polymers are macromolecules that adapt their functionality in response to exposure to certain triggers/stimuli and can thus be exploited for numerous applications, such as sensing, robotics, drug-delivery, and separations. The prospect of tailoring the chemistry and architecture of a stimuli-responsive polymer to elicit a specific, desired functional response is highly enticing; however, there are no existing robust, predictive frameworks to inform their design in a high-dimensional parameter space. As part of the Laboratory Learning program, students will contribute to a research team that aims to resolve key technical bottlenecks that currently inhibit computationally guided design of stimuli-responsive polymers. In particular, major projects include (i) multiscale modeling of thermo-sensitive polymers with expressive coarse-grained potential energy functions, (ii) modeling polymer dynamics in inhomogeneous environments with implicit-solvent frameworks, and (iii) leveraging machine learning to control emergent structural properties of polymeric materials. In aggregate, these activities will provide a foundation for modern computational techniques to be exploited during design of novel smart nanomaterials. Prior participants of the LLP within the Webb group have been challenged to perform a variety of computational tasks, including various programming tasks (mostly shell, Python), machine learning, molecular simulation, and mathematical analysis. Some specific past projects have been on machine learning and modeling of peptide nucleic acid duplex formation/melting, understanding the theoretical underpinnings and manifestation of cosolvency in polymer solutions, databasing and machine learning structure-property relationships in polymeric materials, and implementation and testing of space-filling design of experiments strategies in chemical spaces. The Webb group seeks applicants with strong mathematical background with inclination towards computational research.

Student Roles and Responsibilities: The student will perform regular reporting and updating of results and next steps to PI and graduate student supervisor. The student will need: ability to quickly integrate into team, ability to work independently but recognize when more direction is needed, and critical thinking.

Additional Considerations: Start and end dates are flexible but aim for front half of June. The student should be available essentially full-time (at least 35 hours/week) and is expected to commit for >8 weeks. Precise start date flexible but aim for front half of June.

Department/Institute: Chemical & Biological Engineering

Faculty Sponsor: Michael A. Webb

Participation Dates: 6/3/2024 to 8/12/2024

Stipend Offered: $0

Number of Internships Available: 0-1

Application Deadline: March 15, 2024, midnight eastern daylight time