Modeling High Energy Molecules and Screening to Find Novel High Energy Candidates.

Abstract

High energy materials (HEMs) play pivotal roles in diverse military and civil-commercial sectors, leveraging their substantial energy generation. Integrating machine learning (ML) into HEM research can expedite the discovery of high-energy compounds, complementing or replacing traditional experimental approaches. This manuscript presents an application of our in-house Iterative Stochastic Elimination (ISE) algorithm to identify HEMs. ISE is a generic algorithm that produces reasonable solutions for highly complex combinatorial problems. In molecular discovery, ISE focuses on physicochemical properties to distinguish between different classes of molecules. Due to its long track record in discovering novel, highly active biomolecules, we decided to apply ISE to another type of molecular discovery: High-energy materials. Two distinct ISE models, Model A (92 HEMs) and Model B (169 HEMs), integrated non-HEMs for comprehensive analysis. The results showcase significant achievements for both Models A and B. Model A identified 69% of active molecules in Model B, of which 62% had the highest score. Model B identified 80% of active molecules in Model A, with 61% having the highest score among those 80%. Subsequently, Model C was developed, merging all active molecules (261) from Models A and B. Statistical data indicate that Model C is a high-quality model. It was used to screen and score nearly 2 million molecules from the Enamine database. We find 66 molecules with the highest score of 0.89, plus 8 with that score which are active molecules included in the learning set of Model C. From the 66 molecules, 21 (32%) contain at least one nitro group. In conclusion, this study positions the ISE algorithm as a potential tool for discovering novel HEM candidates, offering a promising pathway for efficient and sustainable advancements in high-energy materials research.