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 disc

SUBMITTER: Rachamim M 

PROVIDER: S-EPMC11500160 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature