(575d) Leveraging Quantum Mechanics for Soft Materials Design

The dramatic progress of soft materials to date has been driven by the use of models based in classical physics. While the successes of these models are beyond reproach, some of the most pressing societal challenges (e.g. sustainability, flexible electronics, energy storage) necessitate the incorporation of quantum mechanics (QM) into the design of soft materials. However, the need for QM predictions applied at the length and time scales characteristic of soft materials represents a substantial computational challenge for which new approaches are needed. I will describe recent work extending and applying QM prediction methods to soft materials design spanning multiple materials classes spanning bond rupture in thermosets, charge conductivity in semiconducting polymers and liquid crystals, and the characterization of the fundamental physical properties across a broad macromolecular genome. Integral to these cases will be the application of advanced machine learning methodologies with new strategies in both “top-down” and “bottom-up” coarse-grained modeling to incorporate electronic predictions.