(523c) Inverse Design of Self-Stratifying Colloidal Films Using Surrogate Modeling on Smolyak Sparse Grids

Self-stratifying films, produced from mixtures of colloidal particles using a single-step drying process, have a variety of potential applications that range from pressure-sensitive adhesives to abrasion-resistant coatings. The final film structure and properties depend sensitively on the particles and the processing conditions, making them challenging to engineer. In this work, we use an inverse-design strategy based on surrogate modeling of a fitness function to identify conditions that will produce a target film structure. We considered a two-component hard-sphere colloidal suspension, whose designable parameters were the sizes of the particles, the initial particle volume fractions, and the film drying rate. Simulations were used to model the film structure as a function of these parameters, and then a surrogate model for the fitness of the film structure was constructed from simulations using polynomial interpolation on Smolyak sparse grids. This surrogate model was iteratively optimized to identify parameters that best reproduced the desired film structure. This study not only has important implications for designing novel self-stratified materials, but also demonstrates a new framework for the inverse design of nonequilibrium assembly processes.