Next-generation membranes for purification and reuse
of highly
contaminated water require materials with precisely tuned functionality
to address key challenges, including the removal of small, charge-neutral
solutes. Bioinspired multifunctional membrane surfaces enhance transport
properties, but the combinatorically large chemical space is difficult
to navigate through trial and error. Here, we demonstrate a computational
inverse design approach to efficiently identify promising materials
and elucidate design rules. We develop a combined evolutionary optimization,
machine learning, and molecular simulation workflow to spatially design
chemical functional group patterning in a model nanopore that enhances
transport of water relative to solutes. The genetic optimization discovers
nonintuitive functionalization strategies that hinder the transport
of solutes through the pore, simply by patterning hydrophobic methyl
and hydrophilic hydroxyl functional groups. Examining these patterns,
we demonstrate that they exploit an unexpected diffusive solute hopping
mechanism. This inverse design procedure and the identification of
novel molecular mechanisms for pore chemical heterogeneity to impact
solute selectivity demonstrate new routes to the design of membrane
materials with novel functionalities. More broadly, this work illustrates
how chemical design is a powerful strategy to modulate water-mediated
surface–solute interactions in complex, soft material systems
that are relevant to diverse technologies.