Produktion av nya energetiska material är dyrt. Målet med detta arbete är att utveckla en metod som uppskattar Chapman-Jouget detonationstrycket och detonationshastigheten hos nya CHNO-sprängämnen. En sådan metod möjliggör valet att bara syntetisera och testa de mest lovande kandidaterna som nya energetiska material, därmed undviks resursslösande arbete. Den här metoden använder sig av density functional theory (DFT) och den elektrostatiska potentialen till att uppskatta bildningsentalpier och kristalldensiteter. Beräkningarna gjordes på traditionella sprängämnen och de beräknade detonationstrycken och detonatioshastigheterna visade bra överensstämmelse med experimentell data. Detonationstrycken hamnade inom ±10% och detonatioshastigheterna inom ±5% av experimentell data. / Producing new energetic materials is expensive. This work aims to develop a method of estimating the Chapman-Jouget detonation pressure and the detonation velocity of potential new CHNO explosives. This would allow choosing the most promising candidates for synthesis and testing thus avoiding wasting resources. This method utilizes density functional theory (DFT) and the electrostatic potential to estimate heats of formation and crystal densities. The calculations were performed on traditionally used explosives and the calculated detonation pressures and detonation velocities showed good accordance with experimental data. The calculated detonation pressures fell within ±10% and the detonation velocities within ±5% of experimental data.

The aim of this project was to investigate a potential connection between a molecule's capacity to penetrate the blood-brain barrier and its electrostatic potential on the surface of the molecule. A molecule's capacity to penetrate the blood-brain barrier is quantified by their log BB value. This was done by creating a set of 58 molecules, divided into a test set and a calibration set, in Gaussian 16. The molecules were then optimized and their quantum chemical properties were calculated by using HS-95. By using stepwise regression in both Matlab and Excel these different parameters were then used to search for a correlation between the parameters and experimental log BB values. This resulted in an equation with 10 variables with an adjusted R2 value of 0,6456. When tested against the test set the mean fault was 0,1443. Outliers were then identified and removed and stepwise regression was executed once more. This resulted in an equation with 10 variables with an adjusted R2 0,8749. When this was tested against the test set the mean fault was 0,0798. The results showed that the variables that were important were the electrostatic potential, surface area and volume.