Quantum-chemistry informed Bayesian optimization for the accelerated discovery of novel pyrazole-based energetic materials

IF 6.3 Artificial intelligence chemistry Pub Date : 2026-06-01 Epub Date: 2026-02-03 DOI:10.1016/j.aichem.2026.100110
Muktar Musa Ibrahim, Adamu Uzairu, Gideon Adamu Shallangwa, Muhammad Tukur Ibrahim
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Abstract

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/6-311G (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.
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量子化学通知贝叶斯优化加速发现新的吡唑基高能材料
开发具有小环境足迹的高性能高能材料(EMs)仍然是民用和军事应用的关键挑战。研究部门炸药(RDX)和三硝基甲苯(TNT)等传统的EMs表现出很强的性能,但它们往往会释放有毒副产物,造成重大的环境风险。在这项工作中,我们引入了一种量子化学的贝叶斯优化(BO),以加速发现新的吡唑基em。采用BO和量子化学计算来鉴定具有高潜力的电子显微镜应用的吡唑衍生物。利用350个基于吡唑的EMs库,通过系统枚举具有不同爆炸基团的吡唑支架,并计算其密度值。该方法可以在少量迭代中快速识别出具有最佳密度值的吡唑衍生物。在B3LYP/6-311G (d, p)理论水平上,利用密度泛函理论(DFT)计算进一步确定了bo选择的吡唑衍生物的能势。利用扩展的1500个吡唑衍生物库进一步验证了BO框架快速识别高能量势吡唑衍生物的通用性。由BO算法确定的前5个候选爆炸物显示出令人印象深刻的能量潜力,DFT计算的爆炸参数与基准爆炸物相当或超过。通过DFT计算得到的po选择的吡唑衍生物的爆轰参数包括结晶密度(1.93-2.25 g cm−3)、生成热(712.4-876.2 kJ mol−1)、爆轰速度(8.78-9.22 km s−1)和爆轰压力(18.60-25.11 GPa),均有利于EMs的应用。本研究为快速发现新兴市场建立了数据驱动的工作流程,并强调吡唑支架是更安全、更环保的新兴市场平台,与民用和军事应用直接相关。
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Artificial intelligence chemistry
Artificial intelligence chemistry Chemistry (General)
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