Surface modification of water filtration membrane materials has the potential to imbue attractive qualities, such as enhanced antifouling or solute rejection/selectivity capabilities. Development of next-generation membrane materials will therefore rely on design rules relating surface structure and functional properties. Heuristics exist for simple surfaces comprising homogeneous chemistries and have largely been discovered through trial-and-error synthesis and characterization. However, we lack a complete understanding of nanoscopic mechanisms governing the water-mediated interactions between contaminants/foulants and realistic surfaces with chemical and topological heterogeneity. Furthermore, trial-and-error methods are not able to efficiently explore the large design space offered by multifunctional surfaces.

This dissertation develops relationships between surface chemistry and local water/solute behavior for model membrane materials, using computational inverse design approaches. By coupling molecular dynamics simulations with optimization algorithms, we identify non-intuitive chemistries showing enhanced functional properties. Genetic algorithm optimization of patterns of nonpolar and polar chemistries in a model nanopore suggests that arrangement of polar chemistry in rings along the nanopore wall will enhance selectivity of water over small, neutral solutes. Active-learning-based optimization of more complex patterns with both chemical and topological heterogeneity suggest that rings of large nonpolar groups create a rough surface that achieves similar effects. Importantly, these non-intuitive functionalities discovered by the inverse design approach suggest previously unexplored mechanisms governing water-mediated solute transport in model membrane materials.

Sequence-specific polymers such as polypeptoids provide a synthetically accessible route to chemically precise surface modification. However, exploration of sequence-structure-function relationships for polypeptoids has been lacking, in part because computational workflows to simulate polypeptoids are not well established. In collaboration with detailed experimental characterization, we validate an enhanced sampling simulation workflow and atomistic model for polypeptoids. We then show that polypeptoid sequence modulates local water behavior in dilute solution and at peptoid brush surfaces, and that the changes in water structure can be related to trends in affinity of small-molecule solutes for peptoid brush surfaces. Finally, we apply the previously developed inverse design techniques to build sequence-structure relationships by identifying sequences with extremal conformational behaviors.