Chemistry at Liquid Interfaces
Chemical reactions can be accelerated by orders of magnitude at liquid interfaces compared to the bulk liquid. We develop new Machine Learning Potential methods and interfacial liquid state theories to explain when and why this acceleration occurs and how we can harness it. These results help us improve batteries, synthesis techniques and understand crucial atmospheric reactions that impact our environment.
Biological Control of Photosynthesis
Photosynthetic organisms show a remarkable ability to regulate photosynthesis, converting nearly every photon of energy into chemical energy under low light conditions or safely dissipating nearly every photon under high light intensities. We build multi-scale models from the femtosecond dynamics of electrons in the first few femtoseconds after sunlight absorption, to cellular and organism level dynamics that take place over minutes to years to understand the biochemical mechanisms of this control. We analyze these models using non-equilibrium statistical mechanics as a case study of the thermodynamics of homeostasis and biological control.
Noise-Robust Quantum Devices
Whenever a quantum device is placed in a realistic environment, it constantly experiences random kicks and perturbations from its surroundings that destroy the delicate quantum effects on which it relies. This “decoherence” process is one of the key barriers to building realistic quantum computers. Our research develops theories that will pave the way for quantum devices that are robust to noise by (1) building trajectory-based non-equilbirum quantum statistical mechanical theories that bound the physical limits of noisy quantum computers and (2) by devising new strategies for using noise as a resource to generate quantum coherences instead of destroying them, enabling a new generation of quantum technologies that are driven by noise.
