This SRI stream introduces students to the intersection of chemistry, materials science, and automation. Students will use state-of-the-art robotic platforms to rapidly fabricate thin films of organic and hybrid semiconductor materials and characterize their optical, electrical, and structural properties. These materials form the basis for technologies that harvest and store clean energy, such as solar cells, light-emitting diodes, and solid-state batteries. By performing hundreds of controlled experiments in parallel, students will learn how small changes in molecular composition or processing conditions influence material performance and stability.

Beyond experimentation, students will gain experience using data-driven tools to accelerate scientific discovery. They will analyze the results of their experiments with machine learning algorithms to identify trends, optimize performance, and predict new material combinations with desirable properties. This research experience will help students understand how automation and artificial intelligence are transforming materials research, equipping them with valuable skills in programming, data visualization, and experimental design. Ultimately, students will contribute to the broader goal of developing sustainable materials and technologies for a clean energy future.

This is an interdisciplinary project, so we are interested in students planning to major in physics, chemistry, materials science, computer science, computer engineering, and related disciplines.