Abstract
Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor η and a hydrogen-bond descriptor Hb are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for Δρ < 5%, are 92.7%, 91.8%, and 93.3%; for Δρ < 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for Δρ < 5%, the values are 92.3%, 91.4% and 92.9%; for Δρ < 2%, the values are 53.7%, 51.4% and 53.2%.
Highlights
Crystal density is an intrinsic characteristic for solids, which is used as an index to reveal material properties including mechanistic, thermodynamic, etc. [1,2,3,4,5,6,7,8,9,10,11,12]
The effects of molecular morphology and hydrogen bond on crystal density were not considered by the reported DFT + quantitative structure-property relationship (QSPR) methods, which is likely to have an impact on the accuracy of the density prediction
The influence of the two descriptors η and Hb on prediction error indicated that Equations without considering these two factors will result in lower density prediction values, especially for molecules with more ‘bricklike’ shape or more hydrogen bonds
Summary
Crystal density is an intrinsic characteristic for solids, which is used as an index to reveal material properties including mechanistic, thermodynamic, etc. [1,2,3,4,5,6,7,8,9,10,11,12]. In most of the GAM expressions, the descriptors, such as molecular configuration, conformation, and non-bond interaction that have decisive influences on packing pattern of molecules, are missing, and the density prediction errors affected by them are difficult to be corrected through simple empirical formulas. The density prediction method of DFT + QSPR has been widely used in molecule design and performance prediction, its accuracy needs further improvement. Current work intends to improve density prediction models of DFT + QSPR to perform better accuracy and generalization than before, by introduces new molecular descriptors and extends training set to thousands of samples. The effects of molecular morphology and hydrogen bond on crystal density were not considered by the reported DFT + QSPR methods, which is likely to have an impact on the accuracy of the density prediction.
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