Our division focuses on data and ML models to accelerate materials-to-device integration, especially for PEMFC and PEWE. Three core areas are:

• Data extraction and management: includes extracting, collecting, and managing large-scale data from various sources, supported by in-house developed and domain-specific ontology and materials linguistics as well as DL-based data enrichment
• AI/ML models that are currently being used for inverse design of new electrocatalysts and ionic media in PEMWE as well as Redox Flow Batteries Fully automated image analysis
  Accelerated simulations
• ML is used to train predictive models connecting materials properties to characterization and performance data.

We have developed cloud applications that provide users with data accessibility, pre-trained ML models that can run on the fly to user specification, with embedded powerful visualization capabilities