Synergizing Experimentation and Computation: Predicting Energetic Potential in New Cyclo-Peroxide Compounds.

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Materials Science

Background:

• Cyclo-peroxide compounds (CPs) are a class of molecules with potential applications as energetic materials.
• Experimental synthesis and characterization of new CPs can be time-consuming and resource-intensive.
• Predictive models are needed to efficiently screen and prioritize CPs for energetic properties.

Purpose of the Study:

• To synthesize and characterize novel cyclo-peroxide compounds.
• To develop and validate a computational model for predicting the energetic potential of CPs.
• To compare experimental energetic properties with computational predictions.

Main Methods:

• Synthesis of 10 new cyclo-peroxide compounds using various ketones and hydrogen peroxide concentrations.
• Spectroscopic analysis and calorimetric tests (Differential Scanning Calorimetry - DSC) to determine experimental energetic properties (% Power Index - %PI).
• Development and application of the Iterative Stochastic Elimination (ISE) algorithm for computational screening and scoring of CPs.

Main Results:

• Successful synthesis and characterization of 10 new cyclo-peroxide compounds.
• Experimental determination of %PI for the synthesized compounds.
• Validation of the ISE model, demonstrating robust predictive capabilities and consistent correlation with experimental %PI values.
• The ISE model proved efficient in scoring CPs for their energetic potential.

Conclusions:

• The Iterative Stochastic Elimination (ISE) model is a reliable and efficient tool for predicting the energetic potential of cyclo-peroxide compounds.
• Integrating computational (ISE model) and experimental methods enhances the discovery process for energetic materials.
• The ISE model facilitates faster, cost-effective, and safer experimental investigations by focusing on promising candidates.
• Future research is suggested for ester nitrates, further leveraging the combined computational-experimental approach.