Prediction and Construction of Energetic Materials Based on Machine Learning Methods.

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Energetic materials (EMs) are crucial for defense and aerospace, but their discovery traditionally involves lengthy and costly trial-and-error methods.
• Precise molecular design and green synthesis of EMs are significant global research challenges.
• The limitations of experimental approaches necessitate advanced computational tools for efficient material innovation.

Purpose of the Study:

• To review the critical processes involved in applying machine learning (ML) for the discovery and prediction of energetic materials.
• To outline the main ideas and steps for utilizing ML in materials science research.
• To summarize the current state-of-the-art ML applications in predicting EM properties and inverse material design.

Main Methods:

• Data preparation and feature extraction for creating suitable datasets for ML models.
• Construction and selection of appropriate machine learning models (e.g., regression, classification).
• Rigorous model performance evaluation to ensure prediction accuracy and reliability.

Main Results:

• Machine learning methods offer a powerful complement to experimental studies, significantly reducing R&D time and costs.
• ML enables accurate prediction of energetic material properties and facilitates inverse design for targeted applications.
• The review provides a comprehensive overview of ML techniques applied to energetic materials discovery.

Conclusions:

• Machine learning is a transformative tool for accelerating the design and synthesis of advanced energetic materials.
• Addressing current challenges in ML application requires strategic development in data quality, model interpretability, and integration with experimental validation.
• Future research should focus on enhancing ML methodologies for more efficient and sustainable energetic materials development.