First principles molecular dynamics simulation and thermal decomposition kinetics study of CL-20.

Area of Science:

• Computational Chemistry
• Materials Science
• Chemical Engineering

Background:

• 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexazepane (CL-20) is a high-performance, low-sensitivity energetic material.
• Understanding CL-20's thermal decomposition is crucial for enhancing its performance, safety, and applications.
• Traditional empirical force fields have limitations in accurately simulating complex decomposition mechanisms.

Purpose of the Study:

• To investigate the thermal decomposition mechanism of CL-20 using a novel machine learning augmented first-principles molecular dynamics method.
• To identify stable products and intermediates formed during CL-20 decomposition at various high temperatures.
• To elucidate the initial decomposition pathways, including denitration, ring-opening, and redox reactions.

Main Methods:

• Employed a machine learning augmented first-principles molecular dynamics (AIMD) approach for CL-20 simulation.
• Utilized an ab initio Bayesian active learning algorithm with the Vienna Ab-Initio Simulation Package (VASP) to construct the MLFF.
• Simulated molecular dynamics of CL-20 at 2200 K, 2500 K, 2800 K, and 3000 K using the trained MLFF model.

Main Results:

• Identified N2, CO2, CO, H2O, and H2 as the primary stable decomposition products.
• Observed further decomposition of CO2 and H2O at higher temperatures.
• Determined initial decomposition pathways involving N-N fracture (denitration), C-N bond fracture (ring-opening), and redox reactions with NO2.

Conclusions:

• The MLFF-AIMD method provides an accurate and efficient approach to study CL-20 thermal decomposition.
• Ring-opening leads to fused tricyclic pyrazine and azadicyclic structures, further decomposing into monocyclic pyrazine and pyrazole rings.
• The study provides insights into the formation rules and quantities of intermediates and products, aiding in CL-20 optimization.