Machine learning quantitatively characterizes the deformation and destruction of explosive molecules. Issue 12 (9th March 2023)

• Record Type: Journal Article
• Authors: Zhang, Kaining; Chen, Lang; Zhang, Teng; Lu, Jianying; Liu, Danyang; Wu, Junying
• Is Part Of: Physical chemistry chemical physics. Volume 25:Issue 12(2023)
• Page Start: 8692
• Page End: 8704
• Publication Date: 2023-03-09
• Subjects: Chemistry, Physical and theoretical -- Periodicals; 541.3
• DOI: 10.1039/d2cp04623g
• Languages: English
• ISSNs: 1463-9076

Abstract

Abstract : We present a quantitative model for molecular structure deformation using machine learning algorithms and a qualitative model of its relationship with molecular structure destruction. The analysis method is applicable to other materials. Abstract : Although explosives have been widely used in mines, road development, old building demolishing, and munition explosions; currently, how chemical bonds between atoms break and recombine, how the molecular structure is deformed and destroyed, how the reaction product molecules are formed, and the details for this rapid change process in explosive reactions are not yet fully understood, which limits the full use of explosive energy and safer use of explosives. This paper presents a quantitative model of molecular structure deformation using machine learning algorithms as well as a qualitative model of its relationship with molecular structure destruction, based on a molecular dynamics simulation and detailed analysis of the shock-loaded ε-CL-20, providing new perspectives for explosive community research. Specifically, the quantitative model of molecular structure deformation establishes the quantitative relationship between the molecular volume change and molecular position change, and between molecular distance change and molecular volume change using the machine learning algorithms such as Delaunay triangulation, clustering, and gradient descent. We find that the molecular spacing in explosives is strongly compressed after being shocked, and the peripheral structure can shrink inward, which is beneficial to keep the cage structure stable. When the peripheral structure is compressed to a certain extent, the cage structure volume begins to expand and is then destroyed. In addition, hydrogen atom transfer occurs within the explosive molecule. This study amplifies the structural changes and the chemical reaction process for explosive molecules after being strongly compressed by a shock wave, which can enrich the knowledge of the real detonation reaction process. The analysis method based on quantitative characterization using machine learning proposed in this study can also be used to analyze the microscopic reaction mechanism in other materials.