Integrating graph-based data management into materials acceleration platforms

This research project aims to significantly improve the way data are managed in a specific self-driving laboratory in the AUTODIAL group of Prof. Hattrick-Simpers at the University of Toronto, focusing on discovering new materials that are resistant to corrosion. This class of labs, known as Self-driving labs (SDL) or Materials Acceleration Platforms (MAPs), use advanced technologies, such as AI and automated experiments. The project introduces a new system for organizing and analyzing data using a new approach called a graph database, which is better at handling complex and interconnected information. This upgrade will also involve the use of sophisticated language-processing technologies to better understand and utilize the data collected. The goal is to make the labs more efficient and effective, reducing the time and cost of the experiments and simulations. The project will also compare how these labs communicate in an in-operable fashion with similar labs across Canada and Germany to identify common challenges and solutions, ultimately aiming to accelerate the development of new materials.

Faculty Supervisor:

Jason Hattrick-Simpers

Student:

Partner:

Rheinisch-Westfälische Technische Hochschule Aachen

Discipline:

Engineering

Sector:

Energy and Utilities; Technology; Advanced Manufacturing

University:

University of Toronto

Program: