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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: 30 June 2024 | Viewed by 1231

Special Issue Editors

School of Chemistry and Chemical Engineering, North University of China, Taiyuan 030051, China
Interests: molecule design of energetic material; sensitivity under external stimuli; ab initio; DFT; quantum mechanics; molecular dynamics; statistical mechanics; rare event method
Xiʼan Modern Chemistry Research Institute, Xiʼan 710065, China
Interests: quantum chemistry; computing materials; high-energy/density materials; machine learning modelling; thermochemistry and molecular dynamic simulations
State Key Laboratory of Explosion Science and Technology, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Published Papers (1 paper)

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Research

9 pages, 2670 KiB  
Communication
Accelerating the Design of High-Energy-Density Hydrocarbon Fuels by Learning from the Data
by Linyuan Wen, Shiqun Shan, Weipeng Lai, Jinwen Shi, Mingtao Li, Yingzhe Liu, Maochang Liu and Zhaohui Zhou
Molecules 2023, 28(21), 7361; https://doi.org/10.3390/molecules28217361 - 31 Oct 2023
Viewed by 904
Abstract
In the ZINC20 database, with the aid of maximum substructure searches, common substructures were obtained from molecules with high-strain-energy and combustion heat values, and further provided domain knowledge on how to design high-energy-density hydrocarbon (HEDH) fuels. Notably, quadricyclane and syntin could be topologically [...] Read more.
In the ZINC20 database, with the aid of maximum substructure searches, common substructures were obtained from molecules with high-strain-energy and combustion heat values, and further provided domain knowledge on how to design high-energy-density hydrocarbon (HEDH) fuels. Notably, quadricyclane and syntin could be topologically assembled through these substructures, and the corresponding assembled schemes guided the design of 20 fuel molecules (ZD-1 to ZD-20). The fuel properties of the molecules were evaluated by using group-contribution methods and density functional theory (DFT) calculations, where ZD-6 stood out due to the high volumetric net heat of combustion, high specific impulse, low melting point, and acceptable flash point. Based on the neural network model for evaluating the synthetic complexity (SCScore), the estimated value of ZD-6 was close to that of syntin, indicating that the synthetic complexity of ZD-6 was comparable to that of syntin. This work not only provides ZD-6 as a potential HEDH fuel, but also illustrates the superiority of learning design strategies from the data in increasing the understanding of structure and performance relationships and accelerating the development of novel HEDH fuels. Full article
(This article belongs to the Special Issue Molecular Design and Theoretical Investigation of Energetic Materials)
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