idanwekhai / ada

Self-driving laboratory for accelerated discovery of thin-film materials - data repository

Project Ada: public data repository

This repository contains data associated with Project Ada's robotic chemistry and materials science experiments. For more information, see http://www.projectada.ca.

• Taherimakhsousi, N. et al. Quantifying Defects in Thin Films using Machine Vision. npj Computational Materials, s41524-020-00380-w (2020) (GitHub folder)
• MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Science Advances, eaaz8867 (2020) (GitHub folder)
• MacLeod, B. P. & Parlane, F. G. L. et al. A self-driving laboratory advances the Pareto front for material properties. Nature Communications, s41467-022-28580-6 (2022) (GitHub folder)
• Rooney, M. B. & MacLeod, B. P. et al. A self-driving laboratory designed to accelerate the discovery of adhesive materials. Digital Discovery, D2DD00029F (2022) (GitHub folder)
• Rupnow, C. C. et al. A self-driving laboratory optimizes a scalable process for making functional coatings. Cell Reports Physical Science, S2666386423001856 (2023) (GitHub folder)

Note that this repository uses Git LFS for managing larger files.