Reduced dimensionality representations of soft matter

Many phenomena in soft condensed matter occur on length and/or time scales that are unfeasible to study theoretically or computationally if every atom in the system is represented explicitly.

One strategy to address this issue to simplify the representation of a molecular system by systematically eliminating unimportant degrees-of-freedom to improve computational efficiency without sacrificing accuracy.

This project will use methods from statistical mechanics, machine learning, and artificial intelligence to develop algorithms to automate the dimensionality reduction process.

Projects include:

• Identifying the optimal mapping of a molecular system onto a smaller number of ‘coarse-grained’‚ interaction sites, with a focus on anisotropic molecules; or
• Identifying low-dimensional equations of motion from the trajectory of a high-dimensional dynamical system.

Supervisor

Associate Professor David Huang

Research area: Theoretical chemistry, computational chemistry, physical chemistry, chemical physics - Huang Group, School of Physical Sciences

Recommended honours enrolment: Honours in Chemistry or Honours in Physics