Together, these problems place an enormous learning burden on the would-be computational chemist and make the field challenging to break into. Even for more experienced users, the daily process of performing computations can be tedious, time consuming and error prone, because so much of the process needs to be curated manually. Many computational chemists will know the anguish of discovering a mistake in an input file only after the calculation has returned with useless data, wasting days of CPU time, and demanding the entire process be started again. Even when performed correctly, this process is inefficient. Many parts of the pipeline, most noticeably between separate calculation steps, require manual intervention by the chemist to set up the next stage. This results in the overall execution time being limited not by the speed of the computer, but by how attentive the user is in checking the calculation progress. For computational screens involving many molecules, the inefficiencies are multiplied because only one molecule can be prepared by the user at a time. For very large computational projects, this essentially mandates that the scientist writes their own code to help automate the process, but this is not a skill that is accessible to all, nor one that is necessarily linked to scientific proficiency.