The rate of discovery of new explosives with superior energy density and performance has largely stalled. Rapid property prediction through machine learning has the potential to accelerate the discovery of new molecules by screening of large numbers of molecules before they are ever synthesized. To support this goal, we assembled a 21,000-molecule database of experimentally synthesized molecules containing energetic functional groups. Using a combination of experimental density measurements and high throughput electronic structure and atomistic calculations, we calculated detonation velocities and pressures for all 21,000 compounds. Using these values, we trained machine learning models for the prediction of density, detonation velocity and detonation pressure. Notably, our model for crystal density surpassed the accuracy of all current models and decreased the root-mean square error (RMSE) of the previous best model by 20%. This improvement in model performance relative to past works is attributed to our handling of chiral-specified Simplified Molecular-Input Line-Entry System (SMILES) strings and introduction of a new molecular descriptor, MolDensity. To elucidate descriptor importance, we evaluated interpretable descriptors in terms of importance and compared the accuracy of a statistics-driven machine learning model against a model comprised of descriptors typically assumed to control material density. The inexpensive, yet highly accurate predictions from our models should enable creation of future artificial intelligence (AI) models that are able to screen large numbers (>10⁶) of compounds to find the highest performing compounds in terms of crystal density, detonation velocity and detonation pressure.

中文翻译：

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用于高爆炸晶体密度和性能的机器学习模型


具有卓越能量密度和性能的新型炸药的发现速度在很大程度上停滞不前。通过机器学习进行快速性质预测有可能通过在合成大量分子之前筛选来加速新分子的发现。为了支持这一目标，我们组装了一个包含 21,000 个分子的实验合成分子数据库，其中包含高能官能团。结合实验密度测量和高通量电子结构和原子计算，我们计算了所有 21,000 种化合物的爆炸速度和压力。使用这些值，我们训练了机器学习模型来预测密度、爆轰速度和爆轰压力。值得注意的是，我们的晶体密度模型超过了当前所有模型的准确性，并将之前最佳模型的均方根误差 （RMSE） 降低了 20%。相对于过去的工作，模型性能的这种改进归因于我们处理了手性指定的简化分子输入行系统 （SMILES） 字符串，并引入了新的分子描述符 MolDensity。为了阐明描述符的重要性，我们根据重要性评估了可解释的描述符，并将统计驱动的机器学习模型的准确性与通常由通常假定为控制材料密度的描述符组成的模型进行了比较。我们的模型成本低廉但高度准确的预测应该能够创建未来的人工智能 （AI） 模型，这些模型能够筛选大量 （>10⁶） 化合物，以找到在晶体密度、爆轰速度和爆轰压力方面性能最高的化合物。