To date, trial and error strategies guided by intuition have dominated the identification of materials suitable for a specific application. We are entering a data-rich, modeling-driven era where such Edisonian approaches are gradually being replaced by rational strategies which couple predictions from advanced computational screening with targeted experimental synthesis and validation. Consistent with this emerging paradigm, we propose a strategy of hierarchical modeling with successive down-selection stages to accelerate the identification of polymer dielectrics that have the potential to surpass “standard” materials for a given application. Specifically, quantum mechanics based combinatorial searches of chemical and configurational spaces, supplemented with data-driven (machine learning) methods are used. These efforts have led to the identification of several new organic polymer dielectrics within known generic polymer subclasses (e.g., polyurea, polythiourea, polyimide), and the recognition of the untapped potential inherent in entirely new and unanticipated chemical subspaces offered by organometallic polymers. The challenges that remain and the need for additional methodological developments necessary to further strengthen this rational collaborative design concept are then presented.

Scoping the polymer genome: The example of rational computation-guided polymer dielectrics design

255th National Meeting (2018)

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