We are a computational research group leveraging AI and data to understand and design carbon materials for sustainability applications. We curate data from databases and atomistic simulations. We use machine learning tools to explore design space. We apply our research to understand complicated interface chemistry. Our goal is to create the “AIDAM” (AI-Driven Atomistic Materials design) platform which is able to predict the properties of functional carbon materials quickly and accurately.
- High throughput screening and automatic workflow development
- Machine learning for multifunctional materials design
- Fast prediction of complicated interface chemistry