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High Precision Deep-Learning Model Combined with High-Throughput Screening to Discover Fused [55] Biheterocyclic Energetic Materials with Excellent Comprehensive Properties
发布时间：2025-04-30
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- 发布时间：
- 2025-04-30
- 论文名称：
- High Precision Deep-Learning Model Combined with High-Throughput Screening to Discover Fused [55] Biheterocyclic Energetic Materials with Excellent Comprehensive Properties
- 发表刊物：
- RSC Advances
- 摘要：
- 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.
- 合写作者：
- Youhai Liu, Fusheng Yang*, Wenquan Zhang**, Honglei Xia, Zhen Wu, Zaoxiao Zhang
- 是否译文：
- 否
- 发表时间：
- 2024-07-16
