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首页> 外文期刊>The journal of physical chemistry, A. Molecules, spectroscopy, kinetics, environment, & general theory >Machine-Learning Assisted Screening of Energetic Materials
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Machine-Learning Assisted Screening of Energetic Materials

机译：机器学习辅助筛选能量材料

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摘要

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 Delta H-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 Delta H-e training data set, a satisfactory surrogate ML model is trained. The ML model is applied to large databases ICSD and PubChem to predict Delta H-e. At the gross-level filtering by the ML model, 2732 molecular candidates based on carbon, hydrogen, nitrogen, and oxygen (CHNO) with high Delta H-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.
机译：在这项工作中，机器学习（ML），材料信息学（MI）和热化学数据组合到筛选能量材料的潜在候选。为了直接表征能量性能，爆炸ΔH-E的热量用作目标性质。通过大量可能的描述符向前逐步选择，在所有组成元素和氧平衡上平均粘性能量的临界描述符。与它们和理论上标记的Delta H-E培训数据集，训练了令人满意的代理ML模型。 ML模型应用于大型数据库ICSD和Pubchem以预测Delta H-E。在M1型的总级过滤，预测了基于碳，氢气，氮气和氧（CHNO）的2732个分子候选，具有高δH-E值。之后，在2732种材料上进行细水化学筛选，导致TNT等效功率指数P-E（TNT）大于1.5的262名候选。从2732种材料中发现进一步大于1.8，29个潜在候选者的P-E（TNT），所有众所周知的能量材料储层都是新的。

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