In Silico Discovery of Metal-Organic Frameworks for Selective Ion Separation

The treatment and reuse of water mandate the ability to purify this precious resource by selectively removing small ions from solution. Nature has achieved this exquisite selectivity, but manmade materials lag behind despite the primary importance of selective ion separation in scalable materials for water purification and desalination. Metal-organic frameworks (MOFs) represent promising materials for this task because their pores can be tailored to have precise shapes and chemical makeup for selective ion affinity. Nevertheless, the combinatorial space of all conceivable MOFs is vast, and few MOFs have been assessed for their properties relevant to water purification. Many MOFs will break down at reasonable temperatures or otherwise lack stability, including in water.

To unlock the potential of MOFs for water purification, virtual high-throughput screening (VHTS) accelerated by machine learning (ML) models and molecular simulation can accelerate discovery of MOFs that do not have these limitations. Specifically, these VHTS strategies will use ML to leverage existing knowledge about what dictates MOF stability. This research project will develop novel computational strategies to identify optimal MOF materials for water purification by curating and searching a wide space of new "ultrastable" MOF structures.