Our materials simulations and modelling expertise spans from the atomistic to the mesoscale, using a range of computational methods including Density Functional Theory for quantum-level insights, Molecular Dynamics for atomistic interactions, and Dissipative Particle Dynamics models for mesoscale problems. We enhance these simulations with machine learning techniques, for faster and more accurate predictions of material properties and behaviour. Our work focuses on both novel solid-state metal-ion batteries and large-scale flow batteries.
For solid-state metal-ion batteries, the future of technology depends on materials engineered with atomistic precision. The key challenge is to precisely manipulating materials to optimize electrodes and electrodes/solid electrolytes interfaces. Shuxia Tao utilizes first-principles methods, accelerated by machine learning, to connect chemical compositions, structures, and electrochemical performance, guiding the design of novel battery materials. Combined with advanced characterization techniques available in experiments, this approach accelerates the development of high-performance, next-generation solid-state batteries.
In flow batteries, we explore electrolyte flow and reaction kinetics to optimize performance and efficiency. For example, flow batteries use proton-conducting membranes or nanocomposites like Nafion, which efficiently separates anolyte and catholyte while enabling proton transport. Understanding ion transport in these membranes is crucial for improving performance. Alexey Lyulin uses atomistic and mesoscopic simulations to study Nafion and Nafion-based nanocomposites, focusing on polymer/water/filler interfaces. This research helps optimize membrane permeability and proton transport in flow batteries and fuel cells. He also simulates the glass transition in hydrated Nafion, revealing how annealing impacts structure and conductivity.