Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials

Qiaolin Gou; Jing Liu; Haoming Su; Yanzhi Guo; Jiayi Chen; Xueyan Zhao; Xuemei Pu

doi:10.1016/j.isci.2024.109452

Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials

iScience. 2024 Mar 8;27(4):109452. doi: 10.1016/j.isci.2024.109452. eCollection 2024 Apr 19.

Authors

Qiaolin Gou¹, Jing Liu¹, Haoming Su¹, Yanzhi Guo¹, Jiayi Chen¹, Xueyan Zhao², Xuemei Pu¹

Affiliations

¹ College of Chemistry, Sichuan University, Chengdu 610064, China.
² Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang 621900, China.

Abstract

High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R² of 0.98 and MAE of 8.8 kJ mol^-1, significantly outperforming competitive models.

Keywords: Artificial intelligence; Computational materials science; Energy materials; Machine learning.