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Machine learning-assisted high-throughput virtual screening of novel energetic materials
Jing Yang1,2,3, Huiran Wang4, Luyang Zhang4
1Department of Chemistry, Tangshan Normal University, Tangshan, 063000, China. yjlzddove@163.com.
Journal of Molecular Modeling
|June 17, 2026
Summary
Researchers developed a computational method combining density functional theory (DFT) and machine learning (ML) to discover safer, high-energy density materials (HEDMs). This approach efficiently screened over 10,000 compounds, identifying promising candidates with an optimal energy-safety balance.
Area of Science:
- Materials Science
- Computational Chemistry
- Chemical Engineering
Background:
- High-energy density materials (HEDMs) are crucial for defense and industry.
- A key challenge is balancing high energy output with low mechanical sensitivity.
- Traditional methods for HEDM development are inefficient, costly, and risky.
Purpose of the Study:
- To develop an integrated computational strategy using DFT and ML for efficient HEDM discovery.
- To identify novel energetic compounds with an optimal balance of energy and safety.
- To establish a feasible computational paradigm for rapid HEDM design.
Main Methods:
- Constructed a molecular library of over 10,000 derivatives based on a pyrazole scaffold.
- Calculated 10 key properties for 439 representative molecules using DFT (B3LYP/6-31+G(d,p)).
- Evaluated five ML algorithms, selecting XGBoost for high-throughput virtual screening and DFT validation of top candidates.
Main Results:
- The XGBoost model achieved an average test set R² of 0.90, demonstrating high predictive performance.
- High-throughput virtual screening identified 20 promising energetic compounds.
- DFT verification confirmed the reliability and favorable energy-safety balance of the identified candidates.
Conclusions:
- The integrated DFT and ML approach provides a computationally efficient and reliable method for discovering HEDMs.
- This strategy overcomes limitations of traditional methods, enabling rapid identification of high-energy, low-sensitivity materials.
- The study presents a viable computational paradigm for advancing HEDM research and development.
