Self-driving Lab

Objectives:
- Discover new electrolyte compositions.

- Accelerate the electrolyte discovery process.

I led the design and development of a fully autonomous self-driving laboratory for accelerated electrolyte discovery aimed at improving CO₂-to-ethylene electrocatalytic conversion. The project focused on identifying novel electrolyte compositions and developing a high-throughput, AI-driven
discovery methodology.

The resulting platform achieved at least a 15% improvement in ethylene production performance while accelerating electrolyte screening by over 100× with no human intervention and reducing the experimental research footprint by more than 50% per experiment. The system integrated process automation with advanced AI optimization algorithms to enable closed-loop materials discovery, featuring an automated fluid-handling setup with four high-precision vertical syringe pumps and a peristaltic pump controlled via RS232 and RS485 communication protocols. Additional automation included mass flow controllers, power delivery, data acquisition (DAQ), and an integrated gas sensor for high-throughput ethylene concentration screening.

Publication in progress

Top collaborators: Hyeon Seok Lee, Mahyar Rajabi Kochi, Mohammad Zargartalebi, David Sinton