WHAT WE DO
Our approach is composed of three main components:
- Physical Models describing processes taking place in energy storage devices and electrocatalyst systems are derived from sequential multi-scale simulations to explore properties at wide length and time scales.
- The data generated from high-throughput physical models is then used to train Machine Learning (ML) surrogate models (SMs) to explore a vast parameter space at unprecedented speed and lower cost.
- Experimental verification (from collaborators) of theoretical predictions and analysis to have a close-knit feedback loop of synthesis-structure-property-performance relationships.
Electrolyte screening
