  Materials acceleration platforms (MAPs) combine automation and artificial
intelligence to accelerate the discovery of molecules and materials. They have
potential to play a role in addressing complex societal problems such as
climate change. Solar chemicals and fuels generation via heterogeneous CO2
photo(thermal)catalysis is a relatively unexplored process that holds potential
for contributing towards an environmentally and economically sustainable
future, and therefore a very promising application for MAP science and
engineering. Here, we present a brief overview of how design and innovation in
heterogeneous CO2 photo(thermal)catalysis, from materials discovery to
engineering and scale-up, could benefit from MAPs. We discuss relevant design
and performance descriptors and the level of automation of state-of-the-art
experimental techniques, and we review examples of artificial intelligence in
data analysis. Based on these precedents, we finally propose a MAP outline for
autonomous and accelerated discoveries in the emerging field of solar chemicals
and fuels sourced from CO2 photo(thermal)catalysis.
