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 it can be used to select polymers
for multiple applications in the future.