Large scale, renewable energy storage is needed in the coming decades to address the intermittency of solar and wind energy and displace fossil fuels are our main source of energy. Two promising pathways for renewable energy storage are carbon neutral hydrocarbons and a hydrogen energy economy. The main hurdle in implementing these technologies is the cost of electrolyzers/fuel cells along with the overhaul of our current energy infrastructure. To mitigate the cost of the electrolyzers and fuel cells, cheaper, more efficient catalysts need to be discovered. In this work, we use high-throughput methods to screen electrocatalysts for CO2 electrolysis and alkaline hydrogen oxidation in hopes of discovering more active, cheaper catalyst than pure platinum group metals. A database of catalyst’s activities is compiled, and machine learning is applied to show how different models can optimize alloy catalysts and interpret the importance of different physical properties on catalyst activity. Another important aspect of electrocatalysis is the catalyst support material. Amorphous carbon has been used extensively to date as a support, but it has a tendency to corrode and doesn’t strongly stabilize catalysts on the surface. We synthesize and few ceramic catalyst supports (TiNbO2, WC) and test their ability to stabilize Pt nanoparticles on the surface as well as the activity of Pt on each support. Lastly, the work contains a chapter about a cheap, open source potentiostat that was used to teach electrochemistry to university students in East Africa. A demonstration experiment is described as well an explanation about the hardware and software that was created.