In this work, machine learning (ML), materials informatics (MI), and thermochemical data are combined to screen potential candidates of energetic materials. To directly characterize energetic performance, the heat of explosion ΔH_e is used as the target property. The critical descriptors of cohesive energy, averaged over all constituent elements and the oxygen balance, are found by forward stepwise selection from a large number of possible descriptors. With them and a theoretically labeled ΔH_e training data set, a satisfactory surrogate ML model is trained. The ML model is applied to large databases ICSD and PubChem to predict ΔH_e. At the gross-level filtering by the ML model, 2732 molecular candidates based on carbon, hydrogen, nitrogen, and oxygen (CHNO) with high ΔH_e values are predicted. Afterward, a fine-level thermochemical screening is carried out on the 2732 materials, resulting in 262 candidates with TNT equivalent power index P_e(TNT) greater than 1.5. Raising P_e(TNT) further to larger than 1.8, 29 potential candidates are found from the 2732 materials, all are new to the current reservoir of well-known energetic materials.

中文翻译：

---

机器学习辅助的含能材料筛选。

在这项工作中，将机器学习（ML），材料信息学（MI）和热化学数据相结合，以筛选出高能材料的潜在候选对象。为了直接表征能量性能，将爆炸热ΔH _e用作目标属性。通过从大量可能的描述符中进行逐步选择，可以找到在所有组成元素和氧平衡上平均的内聚能的关键描述符。利用它们和理论上标记的ΔH _e训练数据集，可以训练出令人满意的替代ML模型。ML模型应用于大型数据库ICSD和PubChem以预测ΔH _e。在通过ML模型进行的总水平滤波中，预测到具有高ΔH _e值的基于碳，氢，氮和氧（CHNO）的2732个分子候选物。之后，对2732种材料进行了精细的热化学筛选，结果得到262个候选TNT等效功率指数P _e（TNT）大于1.5。将P _e（TNT）提高到大于1.8时，从2732种材料中发现了29种潜在候选物，所有这些都是当前已知的高能材料储库中的新事物。