Challenges and Opportunities for Self-Driving Labs in Perovskite Photovoltaics
Tonio Buonassisi a
a Massachusetts Institute of Technology, Cambridge, MA, USA
Materials for Sustainable Development Conference (MATSUS)
Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
#AMADISTA - Accelerated Materials Discovery Through Automation and Machine Learning
Lausanne, Switzerland, 2024 November 12th - 15th
Organizers: Philippe Schwaller, Tobias Stubhan and Christian Wolff
Invited Speaker, Tonio Buonassisi, presentation 353
DOI: https://doi.org/10.29363/nanoge.matsusfall.2024.353
Publication date: 28th August 2024

During the past decade, automated high-throughput research has evolved from “toy problems” to enabling instances of materials development and translation on compressed timelines. Self-driving labs (SDLs) take this a step further, by integrating automated high-throughput experimental (HTE) hardware with computational planning tools (inverse design algorithms, optimization algorithms, and advanced data-management systems) in closed learning loops. Although the allure of rapid progress pulls interest to SDLs, achieving tangible results requires a strategic coupled investment in both infrastructure and research. This investment necessitates a deliberate, thoughtful approach contrary to the typical rush for immediate outcomes.

 

This talk will provide an overview of the opportunities and many practical challenges when implementing high-throughput autonomous research systems in a university-lab setting, including: designing, troubleshooting, and de-bottlenecking high-throughput synthesis and characterization workflows, overcoming the “synthesis challenge” in its various forms, environmental control during synthesis and post-processing, managing lead safety, and the human element. I will share some modest successes to date, including discovery of new perovskite-inspired materials and optimization of existing ones, and conclude with a perspective of the opportunities ahead.

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