High Precision Deep-Learning Model Combined with High-Throughput Screening to Discover Fused [55] Biheterocyclic Energetic Materials with Excellent Comprehensive Properties
Release Time:2025-04-30
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- Date:
- 2025-04-30
- Title of Paper:
- High Precision Deep-Learning Model Combined with High-Throughput Screening to Discover Fused [55] Biheterocyclic Energetic Materials with Excellent Comprehensive Properties
- Journal:
- RSC Advances
- Summary:
- Finding novel energetic materials with good comprehensive performance has always been challenging because of the low efficiency in conventional trial and error experimental procedure. In this paper, we established a deep learning model with high prediction accuracy by embedded features in Directed Message Passing Neural Networks. The model combined with high-throughput screening was shown to facilitate rapid discovery of fused [5,5] biheterocyclic energetic materials with high energy and excellent thermal stability. Density Functional Theory (DFT) calculations proved that the concerning performances of the targeting molecules are consistent with the predicted results from the deep learning model. Furthermore, 6,7-trinitro-3H-pyrrolo[1,2-b][1,2,4]triazo-5-amine with both good detonation properties and thermal stability was screened out, whose crystal structure and intermolecular interaction were also analyzed.
- Co-author:
- Youhai Liu, Fusheng Yang*, Wenquan Zhang**, Honglei Xia, Zhen Wu, Zaoxiao Zhang
- Translation or Not:
- No
- Date of Publication:
- 2024-07-16
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