Reliable prediction of crystal density of high nitrogen-containing organic compounds as powerful, less sensitive, eco-friendly energetic materials for dependable assessment of their performance

Bionic inspired multifunctional modular energetic materials: an exploration of new generation of application-oriented energetic materials

“…With the development of computational level, theoretical chemistry and high-throughput screen inject new vitality into the search for energetic materials under the consideration of cost and safety. [13][14][15] During the high-throughput screen, machine learning offers a vital tool for effective performance prediction, such as density, [16][17][18] heat of formation, 19,20 detonation properties, [21][22][23] and decomposition temperature. [24][25][26][27] We have explored to some extent in this eld as well: in 2021, we conducted a domain-related knowledge-promoted highthroughput cage scaffold screening from the ZINC15 database containing over 130 000 scaffolds and merged it with a combinatorial design to alleviate the lack of cage energetic materials.…”

Accurate Density Prediction of Sesquiterpenoid HEDFs and the Multiproperty Computing Server SesquiterPre

“…The rapid and accurate evaluation of density is vital for logically selecting new high-performance HEDFs and for calculation of their energy density. Over recent decades, numerous researchers have strived to predict the density of various compounds. − Previously, cubic equations of state (EoS), such as SRK EoS, PR EoS, and simple RM EoS, have been commonly used for hydrocarbon density prediction. Although these EoS methods predict hydrocarbon density with commendable accuracy and a relative error of less than 5%, their calculation process requires the resolution of multiple parameters, leading to low efficiency and complicated utilization.…”