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There is huge scope for the implementation of sustainable methods in the research of new energetic materials. It is certainly one of the most important aspects which must be considered and implemented in current and future modern scientific research. There are a number of ways this can be achieved, and with the development of the program “Research output software for energetic materials based on observational modelling/ machine learning” (RoseBoom^©) it is hoped that the development of new modern energetic materials will be advanced, since it aims to provide access to quick and easy prediction methods which will indicate performance parameters (e.g. the detonation velocity and pressure, the key indicator for the power of an explosive) – before they have been synthesized. The software allows fast estimation of the performance, enthalpy of formation and density of new energetic compounds only based on the structural formula. To do this it combines empirical and machine learning models into one program, that can be used to evaluate performance of new energetic materials before synthesis and after synthesis within experimental uncertainty. The user-friendly design allows fast computation of hundreds of molecules within a few minutes with minimal user-input. A picture of a compound is sufficient, which can be taken using the screenshot function implemented in RoseBoom^©, the molecule can be copied from a molecule editor, or a list of molecules/mixtures can be loaded into the program, obtaining the results in an Excel spreadsheet.