Molecular & Materials Engineering

Overview

Molecular & Materials Engineering is the division where chemistry, physics and engineering meet at the nanoscale. MME researchers design and characterize new substances — polymers, colloidal assemblies, catalysts, piezoelectrics and organic semiconductors — with properties specified in advance rather than discovered by screening. The aspiration is predictive synthesis: know what you want the material to do, understand the relevant physics, build it.

The division's three groups address complementary scales of organization. The Soft Matter & Self-Assembly Group focuses on systems in which entropy and non-covalent forces determine structure: block copolymers that template nanoscale features, colloidal particles that form photonic crystals, and biological membranes that remodel on demand. The Catalysis & Green Chemistry Lab works at the molecular scale of bond-making and bond-breaking, developing heterogeneous and photo-driven catalysts that minimize energy input and avoid hazardous solvents. The Functional Materials Group works from molecule to device, designing organic electronics, ferroelectric ceramics and shape-memory polymers for specific end functions.

MME draws heavily on the Spectroscopy & Analytical Core for NMR, mass spectrometry and X-ray characterization, and on the Advanced Microscopy Centre for nanoscale imaging. The Veyra Biofoundry supports high-throughput synthesis campaigns in the Catalysis & Green Chemistry Lab.

Research themes

• Entropic and enthalpic self-assembly — controlling phase behavior, defect density and long-range order in soft-matter systems through chemistry and boundary conditions.
• Earth-abundant metal catalysis — iron, manganese and copper catalysts for reactions that currently require platinum-group metals.
• Photo-driven and electrochemical synthesis — using photons or electrons as the primary energy source for bond-forming reactions, reducing reliance on thermal routes.
• Responsive and adaptive materials — polymers and ceramics that change stiffness, shape or electronic state in response to a defined stimulus.
• Materials data and prediction — machine-learning models for property prediction, active-learning campaigns for catalyst discovery, and the Veyra Atlas predictor developed with CDS.