  Modern fuel cell technologies use Nafion as the material of choice for the
proton exchange membrane (PEM) and as the binding material (ionomer), used to
assemble the catalyst layers of the anode and cathode. These applications
demand high proton conductivity as well as other requirements. For example, PEM
is expected to block electrons, oxygen, and hydrogen from penetrating and
diffusing while the anode/cathode ionomer should allow hydrogen/oxygen to move
easily, so that they can reach the catalyst nanoparticles. Given some of the
well-known limits of Nafion, such as low glass-transition temperature, the
community is in the midst of an active search for Nafion replacements. In this
work, we present an informatics-based scheme to search large polymer chemical
spaces, which includes establishing a list of properties needed for the
targeted applications, developing predictive machine-learning models for these
properties, defining a search space, and using the developed models to screen
the search space. Using the scheme, we have identified 60 new polymer
candidates for PEM, anode ionomer, and cathode ionomer that we hope will be
advanced to the next step, i.e., validating the designs through synthesis and
testing. The proposed informatics scheme is generic, and can be used to select
polymers for multiple applications in the future.
