Advancing Self-Driving Laboratories: Benchmarking and Harmonizing Electrochemical Materials Acceleration Platforms

Corrosion—the gradual environmental degradation of metals and alloys—costs Canada billions of dollars annually and threatens infrastructure, transportation, and energy systems. Understanding how materials behave in corrosive environments is complex, depending on factors such as composition, surface structure, and environmental chemistry. Traditional experiments are slow, labor-intensive, and often difficult to reproduce between laboratories.

This project aims to advance autonomous materials research platforms, known as Materials Acceleration Platforms (MAPs), for corrosion science. By comparing MAPs at the University of Toronto and the Federal Institute for Materials Research and Testing (BAM) in Germany, the research will identify sources of variability and develop strategies to harmonize workflows, data collection, and analysis.M achine learning-guided experimentation and high-throughput electrochemical testing, the project will produce standardized datasets and benchmarking protocols, towards enabling autonomous labs to share insights and inform one another.

The outcomes will investigate and provide insights on reproducibility, towards accelerating the discovery of corrosion-resistant materials. By combining AI-driven experimentation with advanced electrochemical expertise, this project strengthens Canada’s innovation capacity in autonomous materials research and contributes to global efforts to standardize self-driving laboratories in materials science.

Faculty Supervisor:

Jason Ryan Hattrick-Simpers

Student:

Partner:

Federal Institute for Materials Research and Testing

Discipline:

Engineering

Sector:

Education

University:

University of Toronto

Program: