(209d) Transitioning from ‘Traditional’ to Data-Driven 'wet’ Laboratories: Growing-Pains and Future Outlook | AIChE

(209d) Transitioning from ‘Traditional’ to Data-Driven 'wet’ Laboratories: Growing-Pains and Future Outlook


Pozzo, L. - Presenter, University of Washington
Rodriguez, J. Jr., University of Washington
Scheiwiller, S., University of Washington
Wolf, C., University of Washington
Politi, M., University of Washington
Bonageri, S., University of Washington
Lachowski, K., University of Washington
The adoption of data-science methods within chemical engineering is primed to revolutionize materials discovery and to accelerate developments in molecular understanding. Yet, the movement is largely dominated by data and information originating from molecular modelling and simulation, or that is acquired from legacy sources (e.g. databases, literature mining) accumulating data from decades of careful experimental work. In order to advance data-driven materials developments well into the future, high-throughput experimentation (HTE) and automation throughout the complete laboratory workflow must also be developed and widely adopted to accelerate rates of experimental data production. In this talk we outline several examples and experiences showcasing how our research group is developing and adapting hardware and software infrastructure to accelerate the pace of molecular discovery in soft-matter systems for applications in health care, clean energy and materials synthesis. The talk will highlight recent research examples related to the implementation of HTE for electrolyte discovery and colloidal formulation/synthesis. We will also highlight significant challenges that have emerged from transitioning an established ‘wet-laboratory’ practice to HTE. These relate to adapting routine experimental methods to achieve HTE, developing new skills within the research workforce, the adoption of new data stewardship practices, needs for autonomous data sorting/classification, algorithms for automatic modeling and analysis and many others. Conversely, we will also highlight the numerous opportunities that emerge for enhancing virtual collaboration, enabling open data/hardware/software sharing, tackling challenging irreducible problems (e.g. optimization of complex formulations), and improving the outlook for the implementation of 'self-driving' laboratories.