Computational DFT studies on a series of toluene derivatives as potential high energy density compounds

Li, Xiaohong; Fu, Zhumu; Zhang, Xianzhou

doi:10.1007/s11224-011-9897-6

Cited by 12 publications

(2 citation statements)

References 34 publications

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“…The reported compound b8 with the same parent ring as a8 but with one −H rather than one −OH substituted at the same position gets a detonation velocity of 9.107 km/s, slightly lower than a8. In addition, the detonation velocities of the reported compounds b9 and b10 71,72 with the same parent ring as a9 and a10 but with the −CH 3 and the −H (rather than −OCH 2 C(NO 2 ) 3 or −N(NO 2 ) 2 ) substituted at the same substitution site are 7.972 and 8.428 km/s, respectively, lower than those of a9 and a10 generated, confirming the importance of the two energetic substituents. Collectively, the 10 molecules generated present higher detonation velocities than the similar molecules reported, showcasing the advantage of our model.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

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Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime

Wang

Sun

et al. 2022

J. Chem. Inf. Model.

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Motivated by the challenging of deep learning on the low data regime and the urgent demand for intelligent design on highly energetic materials, we explore a correlated deep learning framework, which consists of three recurrent neural networks (RNNs) correlated by the transfer learning strategy, to efficiently generate new energetic molecules with a high detonation velocity in the case of very limited data available. To avoid the dependence on the external big data set, data augmentation by fragment shuffling of 303 energetic compounds is utilized to produce 500,000 molecules to pretrain RNN, through which the model can learn sufficient structure knowledge. Then the pretrained RNN is fine-tuned by focusing on the 303 energetic compounds to generate 7153 molecules similar to the energetic compounds. In order to more reliably screen the molecules with a high detonation velocity, the SMILE enumeration augmentation coupled with the pretrained knowledge is utilized to build an RNN-based prediction model, through which R 2 is boosted from 0.4446 to 0.9572. The comparable performance with the transfer learning strategy based on an existing big database (ChEMBL) to produce the energetic molecules and drug-like ones further supports the effectiveness and generality of our strategy in the low data regime. High-precision quantum mechanics calculations further confirm that 35 new molecules present a higher detonation velocity and lower synthetic accessibility than the classic explosive RDX, along with good thermal stability. In particular, three new molecules are comparable to caged CL-20 in the detonation velocity. All the source codes and the data set are freely available at https://github.com/wangchenghuidream/ RNNMGM.

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Section: ■ Results and Discussionmentioning

confidence: 99%

“…Structures of the top 10 molecules in the detonation velocity ranking and similar compounds reported. The pale green and light blue backgrounds denote the molecules generated by the work and the similar molecules reported, − respectively. D and BDE denote the detonation velocity and bond dissociation energy, respectively.…”

Section: Resultsmentioning

confidence: 99%

Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime

Wang

Sun

et al. 2022

J. Chem. Inf. Model.

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Comparative theoretical studies of energetic pyrazole-pyridine derivatives

Zhao

2013

J Mol Model

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The pyrazole-pyridine derivatives were optimized to obtain their molecular geometries and electronic structures at the DFT-B3LYP/6-31G(d,p) and DFT-B3P86/6-31G(d,p) levels. Molecular mechanics (MM) calculations were performed for the title compounds. Heats of formation (HOFs) were predicted through designed isodesmic reactions. Detonation performance was evaluated by using the Kamlet-Jacobs equations based on the calculated densities and heats of formation. The thermal stability of the title compounds was investigated via the bond dissociation energies (BDEs). The simulation results reveal that the compound with one pyrazole ring that is fully nitro-substituted performs similarly to the famous explosive HMX, and the compound with two pyrazole rings that are fully nitro-substituted outperforms HMX. According to the quantitative standard of energetics and stability as high energy density materials (HEDMs), the compound with two pyrazole rings that are fully nitro-substituted essentially satisfies this requirement.

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Interplay of thermochemistry and Structural Chemistry, the journal (volume 23, 2012, issues 1–3) and the discipline

et al. 2012

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Computational DFT studies on a series of toluene derivatives as potential high energy density compounds

Cited by 12 publications

References 34 publications

Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime

Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime

Comparative theoretical studies of energetic pyrazole-pyridine derivatives

Interplay of thermochemistry and Structural Chemistry, the journal (volume 23, 2012, issues 1–3) and the discipline

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