Using supercomputing infrastructure and state-of-the-art atomistic simulations we deliver molecular-level insight into phenomena that drive the performance of energy materials.
Accelerating discovery, design and integration of new electrochemical energy materials by extracting knowledge from large-scale data assets and utilizing advanced AI-driven models.
Physical-mathematical models of performance and degradation in electrodes and cells of electrochemical energy devices and model-based diagnostic methods and tools.
Develop models for dynamic materials phenomena in electrochemical systems and solve them using methods from theoretical and mathematical physics.
