Quantifying bond strengths via a Coulombic force model: application to the impact sensitivity of nitrobenzene, nitrogen-rich nitroazole, and non-aromatic nitramine molecules

The effect of the electronic structure method and basis set on the accuracy of the electric multipoles computed with the distributed multipole analysis (DMA)

CONTEXT

An accurate description of the molecular charge density is crucial for investigating intra- and inter-molecular properties. Among the different ways of describing and analyzing it, the widely used distributed multipole analysis (DMA) is an accurate method for decomposing the molecular charge density into atom-centered electric multipoles (monopole, dipole, quadrupole, and so on) that have a direct chemical interpretation. In this work, DMA was employed to decompose the molecular charge density of six chemically distinct molecules, namely, (2R)-2-amino-3-[(S)-prop-2-enylsulfinyl] propanoic acid (AAP), 4-amine-2-nitro-1,3,5 triazole (ANTA), (RS)-Propan-2-yl methylphosphonofluoridate (SARIN), chloromethane (CLMET), and 2-aminoacetic acid (GLY) into monopole, dipole, and quadrupole values. A hypothetical variation of ANTA built by exchanging all the nitrogen atoms with phosphorus that we named 4-phosphine-2-phosphite-1,3,5-phosphorine (ANTAP) was also studied. These molecules have different chemical structures bearing distinct carbon skeletons, electronegative atoms, and electron-withdrawing/donating groups. We found that although DFT multipole values can depend considerably on the exchange-correlation functional for specific atomic sites, the associated root-mean-square errors (RMSEs) compared to benchmark MP4 mainly were about [Formula: see text] The most significant variations were for monopoles and dipoles of sites highly polarized by adjacent atoms, and to a lesser degree, for the quadrupoles. The double hybrid B2PLYP and the hybrid meta M06-2X functionals, as expected in the framework of Jacob's ladder, overall give the most accurate results among the DFT methods. The MP2 DMA multipole values have an RMSE in relation to the MP4 benchmark mainly in the range [Formula: see text], thus representing a lower computational cost to obtain results with similar good accuracy without the ambiguity of choosing a DFT functional. The deviations of the HF multipoles from the benchmark in most cases were less than 20%, in agreement with the well-known fact that non-correlated charge densities have a slight dependence on the electronic correlation. We also confirmed that DMA values have a small dependence on the size of the basis set: deviations did not exceed 5% in most cases. However, the dependence of the DMA values on the size of the basis set increases with the rank of the electric multipole. To compute accurate values of DMA multipoles of an atom bonded to very electronegative atoms, especially dipoles and quadrupoles, a large basis set including diffuse functions is necessary. Despite that, for a given polarized basis set, the choice of the basis set to compute accurate DMA multipole values is not critical.

METHODS

The molecular charge densities were computed using the electronic structure methods Hartree-Fock (HF), MP2, MP4, DFT/PBE, DFT/B3LYP, DFT/B3PW91, DFT/M06-2X, and DFT/B2PLYP implemented in the Gaussian 09 package. MP4 was the benchmark method. The DMA multipoles were obtained with the GDMA program of Stone. The 6-311G +  + (d,p) basis set was used for the production calculations, and the augmented correlation-consistent Dunning's hierarchy of basis sets was employed to evaluate the dependence of the DMA multipoles on the basis set size.

Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives

We decomposed density functional theory charge densities of 53 nitroaromatic molecules into atom-centered electric multipoles using the distributed multipole analysis that provides a detailed picture of the molecular electronic structure. Three electric multipoles, (the charge of the nitro groups), (the total dipole, i.e., polarization, of the nitro groups), (the total electron delocalization of the C ring atoms), and the number of explosophore groups (#NO₂) were selected as features for a comprehensive machine learning (ML) investigation. The target property was the impact sensitivity h₅₀ (cm) values quantified by drop-weight measurements, with a large h₅₀ (e.g., 150 cm) indicating that an explosive is insensitive and vice versa. After a preliminary screening of 42 ML algorithms, four were selected based on the lowest root mean square errors: Extra Trees, Random Forests, Gradient Boosting, and AdaBoost. Compared to experimental data, the predicted h₅₀ values of molecules having very different sensitivities for the four algorithms have differences in the range 19-28%. The most important properties for predicting h₅₀ are the electron delocalization in the ring atoms and the polarization of the nitro groups with averaged weights of 39% and 35%, followed by the charge (16%) and number (10%) of nitro groups. A significant result is how the contribution of these properties to h₅₀ depends on their actual sensitivities: for the most sensitive explosives (h₅₀ up to ∼50 cm), the four properties contribute to reducing h₅₀, and for intermediate ones (∼50 cm ≲ h₅₀ ≲ 100 cm) #NO₂ and contribute to increasing it and the other two properties to reducing it. For highly insensitive explosives (h₅₀ ≳ 200 cm), all four properties essentially contribute to increasing it. These results furnish a consistent molecular basis of the sensitivities of known explosives that also can be used for developing safer new ones.