Liu Haitao, Chen Peng, Zhang Chaoyang, Huang Xin
Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), Mianyang 621900, PR China.
School of National Defense & Nuclear Science and Technology, Southwest University of Science and Technology, Mianyang 621010, PR China.
J Phys Chem A. 2024 Oct 17;128(41):9045-9054. doi: 10.1021/acs.jpca.4c04849. Epub 2024 Oct 8.
Thermal resistance of energetic materials is critical due to its impact on safety and sustainability. However, developing predictive models remains challenging because of data scarcity and limited insights into quantitative structure-property relationships. In this work, a deep learning framework, named EM-thermo, was proposed to address these challenges. A data set comprising 5029 CHNO compounds, including 976 energetic compounds, was constructed to facilitate this study. EM-thermo employs molecular graphs and direct message-passing neural networks to capture structural features and predict thermal resistance. Using transfer learning, the model achieves an accuracy of approximately 97% for predicting the thermal-resistance property (decomposition temperatures above 573.15 K) in energetic compounds. The involvement of molecular descriptors improved model prediction. These findings suggest that EM-thermo is effective for correlating thermal resistance from the atom and covalent bond level, offering a promising tool for advancing molecular design and discovery in the field of energetic compounds.
含能材料的热稳定性因其对安全性和可持续性的影响而至关重要。然而，由于数据稀缺以及对定量结构-性质关系的认识有限，开发预测模型仍然具有挑战性。在这项工作中，提出了一个名为EM-thermo的深度学习框架来应对这些挑战。构建了一个包含5029种CHNO化合物（其中包括976种含能化合物）的数据集以促进本研究。EM-thermo采用分子图和直接消息传递神经网络来捕捉结构特征并预测热稳定性。通过迁移学习，该模型在预测含能化合物的热稳定性属性（分解温度高于573.15 K）方面达到了约97%的准确率。分子描述符的加入提高了模型预测能力。这些发现表明，EM-thermo在从原子和共价键层面关联热稳定性方面是有效的，为推进含能化合物领域的分子设计和发现提供了一个有前景的工具。
