Quantum-chemistry informed Bayesian optimization for the accelerated discovery of novel pyrazole-based energetic materials
Developing high-performance Energetic Materials (EMs) with small environmental footprint remains a critical challenge for both civilian and military applications. The conventional EMs such as Research Department Explosive (RDX) and trinitrotoluene (TNT) exhibit strong performance, but they tend to release toxic byproducts, posing significant environmental risk. In this work, we introduce a quantum-chemistry informed Bayesian Optimization (BO) for accelerated discovery of novel pyrazole-based EMs. The employed BO and Quantum chemistry calculations to identify pyrazole derivatives with high potential for EMs applications. Using a library of 350 pyrazole-based EMs generated through systematic enumeration of pyrazole scaffold with various explosophoric groups, and their computed density values. BO was employed to rapidly identify pyrazole derivatives with optimal density values in the chemical space within only few iterations. The energetic potential of the BO-selected pyrazole derivatives was further ascertained using density functional theory (DFT) calculations at the B3LYP/6-311 G (d, p) level of theory. The generalizability of the BO framework to rapidly identify pyrazole derivatives with high energetic potential was further validated using an expanded library of 1500 pyrazole derivatives. The top five candidates identified by the BO algorithm demonstrates impressive energetic potential with DFT computed detonation parameters comparable or exceeding those of benchmarked explosives. The DFT computed detonation parameters of the BO-selected pyrazole derivatives including crystalline density (1.93-2.25 g cm -3 ), heats of formation (712.4-876.2 kJ mol -1 ), detonation velocities (8.78-9.22 km s -1 ), and detonation pressure (18.60-25.11 GPa) were found to be favorable for EMs applications. This study establishes a data-driven workflow for rapid EMs discovery and highlights pyrazole scaffolds as promising platforms for safer, greener EMs, with direct relevance for civilian and military applications.
Authors
- Muhammad T. Ibrahim
- Giddeon Adamu Shallangwa
- Muktar Musa Ibrahim
- Adamu Uzairu
Institutions
- Ahmadu Bello University (NG)
Publication Details
- Journal
- Artificial Intelligence Chemistry
- Published
- 2026-02-04
- DOI
- https://doi.org/10.1016/j.aichem.2026.100110
- Primary Topic
- Energetic Materials and Combustion
- Type
- article
- Field-Weighted Citation Impact
- 0.00