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国际能源材料和化学驱动期刊

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ISSN 打印: 2150-766X

ISSN 在线: 2150-7678

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 0.7 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.1 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00016 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.18 SJR: 0.313 SNIP: 0.6 CiteScore™:: 1.6 H-Index: 16

Indexed in

APPLICATIONS OF ENERGETIC MATERIALS BY A THEORETICAL METHOD (DISCOVER ENERGETIC MATERIALS BY A THEORETICAL METHOD)

卷 12, 册 3, 2013, pp. 197-262
DOI: 10.1615/IntJEnergeticMaterialsChemProp.2013006517
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摘要

It has been shown that quantum chemical calculations can be used to Discover Energetic Materials by a Theoretical Method (DEMTM). The key quantity computed is the heat of formation. The computer program CHEETAH converts the heat of formation into calculated explosive properties. We test DEMTM for obtaining explosive properties against some 109 explosive materials and produce thereby their energetic characteristics. Thus, we have shown that DEMTM is appropriate to be used in a search across a database of materials to assess their usefulness as explosives.

对本文的引用
  1. Chen Guang, Shen Zhiqiang, Iyer Akshay, Ghumman Umar Farooq, Tang Shan, Bi Jinbo, Chen Wei, Li Ying, Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges, Polymers, 12, 1, 2020. Crossref

  2. Boukouvalas Zois, Puerto Monica, Elton Daniel C., Chung Peter W., Fuge Mark D., Independent Vector Analysis for Molecular Data Fusion: Application to Property Prediction and Knowledge Discovery of Energetic Materials, 2020 28th European Signal Processing Conference (EUSIPCO), 2021. Crossref

  3. Bier Imanuel, Marom Noa, Machine Learned Model for Solid Form Volume Estimation Based on Packing-Accessible Surface and Molecular Topological Fragments, The Journal of Physical Chemistry A, 124, 49, 2020. Crossref

  4. Nguyen Phan, Loveland Donald, Kim Joanne T., Karande Piyush, Hiszpanski Anna M., Han T. Yong-Jin, Predicting Energetics Materials’ Crystalline Density from Chemical Structure by Machine Learning, Journal of Chemical Information and Modeling, 61, 5, 2021. Crossref

  5. Balakrishnan Sangeeth, VanGessel Francis G., Boukouvalas Zois, Barnes Brian C., Fuge Mark D., Chung Peter W., Locally Optimizable Joint Embedding Framework to Design Nitrogen‐rich Molecules that are Similar but Improved, Molecular Informatics, 40, 7, 2021. Crossref

  6. Maan Anjali, Ghule Vikas D., Dharavath Srinivas, Computational Evaluation of Polycyclic Bis-Oxadiazolo-Pyrazine Backbone in Designing Potential Energetic Materials, Polycyclic Aromatic Compounds, 2022. Crossref

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