The relationships between the theoretical values calculated using density functional theory and experimental data derived from the differential scanning calorimetry of high-energy organic compounds are studied. The theoretical values are the number of atoms and bonds of different types and their lengths, minimum eigenfrequencies, atomization energies, ionization potentials, electron affinities, and frontier orbital energies. The experimental data are the amounts of releasing heat (the first peaks higher than 1 kJ g⁻¹) and corresponding temperatures. Neural networks and regression, factor, discriminant, and cluster analysis are applied to find the dependencies between theoretical values and experimental data. It is found that the heat amount cannot be predicted in the general cases, whereas the corresponding temperature can be predicted with a neural network with an accuracy of ≈30 °C. Cluster and discriminant analysis provides the way for the classification of high-energy compounds into three groups. Some of these groups require particular rules for the prediction of experimental data from the theoretical values.

中文翻译：

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探索神经网络作为从 Ab Initio 相关特性到高能化合物差示扫描量热测量的桥梁

研究了使用密度泛函理论计算的理论值与高能有机化合物的差示扫描量热法得出的实验数据之间的关系。理论值是不同类型的原子和键的数量及其长度、最小本征频率、原子化能量、电离势、电子亲和力和前沿轨道能量。实验数据是放热量（第一个峰值高于 1 kJ g ^-1) 和相应的温度。应用神经网络和回归、因子、判别和聚类分析来寻找理论值和实验数据之间的依赖关系。发现在一般情况下无法预测热量，而使用神经网络可以预测相应的温度，精度约为30°C。聚类和判别分析提供了将高能化合物分为三组的方法。其中一些组需要特定的规则来根据理论值预测实验数据。