Molecular Design and Theoretical Investigation of Energetic Materials
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Materials Chemistry".
Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 6049
Special Issue Editors
Interests: molecule design of energetic material; sensitivity under external stimuli; ab initio; DFT; quantum mechanics; molecular dynamics; statistical mechanics; rare event method
Interests: quantum chemistry; computing materials; high-energy/density materials; machine learning modelling; thermochemistry and molecular dynamic simulations
Interests: molecule design; energy and sensitivity; ab initio; DFT; quantum mechanics; molecular dynamics; statistical mechanics
Special Issue Information
Dear Colleagues,
Energy and sensitivity (safety) are the two most important concerns in dealing with energetic materials, and there exists an inevitable and inherent contradiction regarding the greater thermodynamic energy storage/release and kinetics acceleration/deceleration emerging in chemical reactions under external stimuli, with structure-level dependence at the molecular, crystal and mixture states, and the irreconcilability on the time scale and space scale in the reaction and detonation process, respectively.
Reconciling contradictions is critical to increase the density and energy, reduce crystal defects and sensitivity and improve the detonation performance of energetic materials. Fortunately, several quantitative structure–property relationships (QSPRs) have been identified between the spatial and electronic structures of energetic molecules and their energies and sensitivity performances. Fast-developing machine learning technologies can provide alternative approaches to constructing mathematical models to map microscopic structures to the macroscopic performances of energetic materials based on automatic learning processes using a specific database. The obtained machine learning models could in turn aid our understanding of chemical mechanisms that affect the energy and sensitivity behaviours of energetic materials. In this way, machine learning techniques can be applied to the design of high-energy low-sensitivity molecules by accelerating the screening process of molecules with desired properties, or even to realize the inverse prediction of structures with properties as the input. In particular, as a new mode for the research and development of energetic materials, the Energetic Materials Genome Research Program (EMGI) has accelerated high⁃throughput molecule design and screening.
Moreover, phase transformation is an effective way to reconcile contradictions between energy and sensitivity. Furthermore, understanding the mechanism of phase transformation at the molecular level is of great importance for revealing the essence of the structure–property relationship and providing microscopic dynamics and thermodynamics information so as to optimize the technological process and obtain the desired product. However, today it remains extremely difficult to explain how molecules deviate both at the molecular and crystal levels. The time scale is many orders of magnitude lower than that in reality, leading to two challenges: (1) the rare event, which is not easily accessible with standard molecular simulations; and (2) a set of suitable collective variables that describe the reaction coordinate while avoiding dimension explosion.
The aim of this Special Issue is to provide readers with theoretical methods and results to reveal the essence of contradictions and reconcile contradictions between energy and sensitivity by means of traditional quantum mechanics, molecular dynamics, machine learning and rare event methods. Papers exploring new theories, methodology in quantum electronic structure, molecular dynamics and statistical mechanics, as well as data science, theory and computations to clarify the relationship between energy and sensitivity for the energetic materials are welcome.
Dr. Fude Ren
Dr. Chao Chen
Dr. Nan Li
Guest Editors
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Keywords
- molecule design of energetic material
- contradiction between energy and sensitivity
- DFT
- molecular dynamics
- statistical mechanics
- machine learning
- rare event method
- high throughput in EMGI
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