Modern materials processing and technologies often occur far from equilibrium. For instance, the processing of complex materials such as polymer solutions/melts and composites generally occurs under strong deformations and rapid flows, conditions under which equilibrium thermodynamics does not apply. As a result, the ability to control the polymer multiscale structures during formulation, synthesis and processing has been one of the long-standing challenges. In the current paradigm, newly designed materials that promise improved functionalities must first be subjected to a years-long processing effort to meet the performance targets. One of the major hurdles in meeting these performance goals is the high dimensionality of the searching space associated with the formulation, reaction and processing variables. To bring quantitative insights to the relative importance and specific influencing behaviors of all variables on the structure and property in a time-efficient fashion, we develop a new experimental paradigm that combines the strengths of multiscale computational methods, rapid and robust experiment acquisition from robotic technologies (i.e., high-throughput processing and characterization) and fast analysis of complex datasets using machine learning (ML).  This talk will introduce the utilization of this self-driving lab - PolyBot (https://www.anl.gov/cnm/polybot) for accelerating polymer discovery towards targeted structures for desired properties.

Autonomous robotic platform (Polybot) for polymer discovery

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.