Increasing demand for ammonia is expected in future years due to its potential as an electrochemical fuel and continued use in growing food for billions of people. Meanwhile, there is a growing need for novel antibiotics in the face of antimicrobial resistance worldwide. In this thesis, novel synthesis methods to address both challenges are explored. First, a novel method of ammonia synthesis is demonstrated using a nitrogen stream running through an underwater electric arc. Variation in ammonia yield is shown for a wide array of parameters, including electrode material and geometric configuration. Yield and energy efficiency are compared to other prominent bench-scale ammonia synthesis techniques in the literature. Second, machine learning analysis is conducted on a dataset of cyanine-derived molecules and their inhibition of bacterial growth. The most performant model is blind-tested against additional data, and then promising candidate molecules are offered for future synthesis and testing.