Accelerated Materials Discovery and Optimization with a Self-Driving Fluidic Lab
Milad Abolhasani a
a North Carolina State University, Department of Chemical and Biomolecular Engineering, Partners Way, 911, Raleigh, United States
Materials for Sustainable Development Conference (MATSUS)
Proceedings of MATSUS Spring 2024 Conference (MATSUS24)
#AI - Automation and Nanomaterials (machine learning, artificial intelligence, robotics, accelerated discovery)
Barcelona, Spain, 2024 March 4th - 8th
Organizers: Ivan Infante and Oleksandr Voznyy
Invited Speaker, Milad Abolhasani, presentation 059
DOI: https://doi.org/10.29363/nanoge.matsus.2024.059
Publication date: 18th December 2023

Accelerating materials discovery as well as green and sustainable ways to manufacture them will have a profound impact on the global challenges in energy, sustainability, and healthcare. The current human-dependent paradigm of experimental research in chemical and materials sciences fails to identify technological solutions for worldwide challenges in a short timeframe. This limitation necessitates the development and implementation of new strategies to accelerate the pace of materials discovery. Recent advances in reaction miniaturization, automated experimentation, and data science provide an exciting opportunity to reshape the discovery, development, and manufacturing of new advanced functional materials related to energy transition and sustainability. In this talk, I will present a Self-Driving Fluidic Lab for accelerated discovery, optimization, and manufacturing of emerging advanced functional materials with multi-step chemistries, through the integration of flow chemistry, online characterization, and artificial intelligence (AI).1-3 I will discuss how modularization of different synthesis and processing stages in tandem with a constantly evolving AI-assisted modeling and decision-making under uncertainty can enable resource-efficient navigation through high dimensional experimental design spaces. Example applications of SDFL for the autonomous synthesis of clean energy nanomaterials will be presented to illustrate the potential of autonomous labs in reducing materials discovery timeframe from +10 years to a few months. Finally, I will present the unique reconfigurability aspect of self-driving fluidic labs to close the scale gap in clean energy materials research through on-demand switching from reaction exploration/exploitation to smart manufacturing mode.

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