Abstract
Investigators discuss new findings in artificial intelligence. According to news reporting from Mianyang, People's Republic of China, by NewsRx journalists, research stated, "Exploring the application of machine learning (ML) in energetic materials (EMs) has been a hot research topic." Financial supporters for this research include National Natural Science Foundation of China. Our news journalists obtained a quote from the research from China Academy of Engineering Physics: "Accordingly, the prediction of the detonation properties of EMs using ML methods has attracted much attention. However, the predictive models for the thermal decomposition temperatures (Td) of EMs have been scarcely reported. Furthermore, the small datasets used in these reports lead to a weak generalization ability of the predictive models. This study created a dataset containing 1022 energetic molecules with Td values of 38-425 °C and determined an optimal predictive model through training. The gradient boost machine for regression (GBR) model yielded a coefficient of determination (R2) of 0.65 and a mean absolute error (MAE) of 27.7 for the test set. This study further explored critical features, determining that the prediction accuracy of the models was significantly influenced by descriptors representing molecular bond stability (i.e., the BCUT metrics) and atomic composition (i.e., the Molecular ID)."
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