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

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 experiment...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Mazal Rachamim (19838851) (author)
مؤلفون آخرون:	Abraham J. Domb (639209) (author), Amiram Goldblum (2191642) (author)
منشور في:	2024
الموضوعات:	Biochemistry Biotechnology Cancer Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified statistical data indicate produces reasonable solutions play pivotal roles long track record 32 %) contain highly active biomolecules substantial energy generation integrating machine learning active molecules included find 66 molecules highest score among energy materials research energy materials active molecules 66 molecules learning set highest score hem research energy compounds sustainable advancements study positions promising pathway potential tool plus 8 physicochemical properties manuscript presents identified 69 enamine database diverse military discovering novel different classes comprehensive analysis commercial sectors another type 80 %.
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Modeling High Energy Molecules and Screening to Find Novel High Energy Candidates

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