The design of new materials with specific physical, chemical, or biological properties is a central goal of much research in materials and medicinal sciences. Except for the simplest and most restricted cases brute-force computational screening of all possible compounds for interesting properties is beyond any current capacity due to the combinatorial nature of chemical compound space (set of stoichiometries and configurations). Consequently, when it comes to computationally optimizing more complex systems, reliable optimization algorithms must not only trade-off sufficient accuracy and computational speed of the models involved, they must also aim for rapid convergence in terms of number of compounds 'visited'. I will give an overview on recent progress on alchemical first principles paths and gradients in compound space that appear to be promising ingredients for more efficient property optimizations. Specifically, based on molecular grand canonical density functional theory an approach will be presented for the construction of high-dimensional yet analytical property gradients in chemical compound space. Thereafter, applications to molecular HOMO eigenvalues, catalyst design, and other problems and systems shall be discussed.
This report documents research carried out by the author throughout his 3-years Truman fellowship. The overarching goal consisted of developing multiscale schemes which permit not only the predictive description but also the computational design of improved materials. Identifying new materials through changes in atomic composition and configuration requires the use of versatile first principles methods, such as density functional theory (DFT). Using DFT, its predictive reliability has been investigated with respect to pseudopotential construction, band-gap, van-der-Waals forces, and nuclear quantum effects. Continuous variation of chemical composition and derivation of accurate energy gradients in compound space has been developed within a DFT framework for free energies of solvation, reaction energetics, and frontier orbital eigenvalues. Similar variations have been leveraged within classical molecular dynamics in order to address thermal properties of molten salt candidates for heat transfer fluids used in solar thermal power facilities. Finally, a combination of DFT and statistical methods has been used to devise quantitative structure property relationships for the rapid prediction of charge mobilities in polyaromatic hydrocarbons.