Prediction and Construction of Energetic Materials Based on Machine Learning Methods

• Published in: Molecules (Basel, Switzerland) Vol. 28; no. 1; p. 322
• Main Authors: Zang, Xiaowei, Zhou, Xiang, Bian, Haitao, Jin, Weiping, Pan, Xuhai, Jiang, Juncheng, Koroleva, M Yu, Shen, Ruiqi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31.12.2022; MDPI
• Subjects: Chemical properties; Computational chemistry; computer-learned representation; data augmentation; Datasets; Deep learning; Design and construction; Electric properties; energetic material; Energetic materials; Energy storage; Explosives; Green design; Hydrolases; Learning algorithms; Machine Learning; Materials; materials discovery and prediction; Materials research; Methods; Performance evaluation; R&D; Research & development; Review; China; energetic material; materials discovery and prediction; data augmentation; machine learning; computer-learned representation
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules28010322

Energetic materials (EMs) are the core materials of weapons and equipment. Achieving precise molecular design and efficient green synthesis of EMs has long been one of the primary concerns of researchers around the world. Traditionally, advanced materials were discovered through a trial-and-error processes, which required long research and development (R&D) cycles and high costs. In recent years, the machine learning (ML) method has matured into a tool that compliments and aids experimental studies for predicting and designing advanced EMs. This paper reviews the critical process of ML methods to discover and predict EMs, including data preparation, feature extraction, model construction, and model performance evaluation. The main ideas and basic steps of applying ML methods are analyzed and outlined. The state-of-the-art research about ML applications in property prediction and inverse material design of EMs is further summarized. Finally, the existing challenges and the strategies for coping with challenges in the further applications of the ML methods are proposed.