Antimicrobial resistance is a growing problem for global health as more pathogens become resistant to existing antibiotics [1]. Nanomaterials such as graphene oxide [2] are promising antimicrobial agents to which microbes have less natural resistance than standard antimicrobial drugs. Current laboratory antimicrobial testing techniques can be insufficient for testing large quantities of potential materials, leading to antimicrobial studies taking far longer than desired. UV-Visible spectroscopy, coupled with machine learning, has the potential to shorten this process dramatically and allow for rapid testing of a multitude of samples for antimicrobial efficacy and the development of antimicrobial resistance [3]. This system of an automated microplate UV-Visible spectrometer and machine-learning techniques has been tested with both organic and inorganic materials and a range of bacteria including model organisms of E. coli and Methicillin-resistant S. aureus and shows great promise in being a new rapid technique for testing the chemical interaction between bacteria and antibiotics.