Event details

Note: Please email Mini Thomas, minit@princeton.edu, for meeting password.

The adoption of data-sciences within chemical engineering and materials science is primed to accelerate materials discovery and developments in molecular understanding. Yet, this movement is still dominated by information originating from molecular modelling, or from physical data that is acquired from legacy sources (e.g. databases, literature mining) accumulating decades of careful experimental work. In order to advance data-driven materials developments 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 I will outline examples and experiences showcasing how researchers in our group (and others) are 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 colloidal formulation/synthesis and electrolyte design. It will also highlight significant challenges that emerge when transitioning from established ‘wet-laboratory’ practices to HTE. These relate to adapting specialized experimental methods to HTE, developing new skills within the research workforce, the adoption of new data stewardship practices, financial and infrastructure obstacles, needs for autonomous data sorting/classification, algorithms for automatic modeling and analysis and many others. Conversely, I will also highlight the numerous opportunities that can emerge for enhancing virtual collaboration, enabling open data/hardware/software sharing, tackling challenging irreducible problems (e.g. optimization of complex formulations), and outlooks for the implementation of self-driving laboratories.