De Deb Sankar, Krummenacher Marco, Schaefer Bastian, Goedecker Stefan (2019), Finding Reaction Pathways with Optimal Atomic Index Mappings, in Physical Review Letters
, 123(20), 206102-206102.
Dutta D., De D. S., Fan D., Roy S., Alfieri G., Camarda M., Amsler M., Lehmann J., Bartolf H., Goedecker S., Jung T. A. (2019), Evidence for carbon clusters present near thermal gate oxides affecting the electronic band structure in SiC-MOSFET, in Applied Physics Letters
, 115(10), 101601-101601.
Graužinytė Miglė, Tomerini Daniele, Goedecker Stefan, Flores-Livas José A. (2019), Divalent Path to Enhance p-Type Conductivity in a SnO Transparent Semiconductor, in The Journal of Physical Chemistry C
, 123(24), 14909-14913.
Flores-Livas José A., Sanna Antonio, Graužinytė Miglė, Davydov Arkadiy, Goedecker Stefan, Marques Miguel A. L. (2017), Emergence of superconductivity in doped H2O ice at high pressure, in Scientific Reports
, 7(1), 6825-6825.
Fisicaro Giuseppe, Sicher Michael, Amsler Maximilian, Saha Santanu, Genovese Luigi, Goedecker Stefan (2017), Surface reconstruction of fluorites in vacuum and aqueous environment, in Physical Review Materials
, 1(3), 033609-033609.
Flores-Livas José A., Sanna Antonio, Drozdov Alexander P., Boeri Lilia, Profeta Gianni, Eremets Mikhail, Goedecker Stefan (2017), Interplay between structure and superconductivity: Metastable phases of phosphorus under pressure, in Physical Review Materials
, 1(2), 024802-024802.
Jensen Stig Rune, Saha Santanu, Flores-Livas José A., Huhn William, Blum Volker, Goedecker Stefan, Frediani Luca (2017), The Elephant in the Room of Density Functional Theory Calculations, in The Journal of Physical Chemistry Letters
, 8(7), 1449-1457.
In atomistic simulations the positions of all the involved atoms are individually known. In this way the structure as well as the dynamics of molecular systems can be studied and understood in depth. A prerequisite for such atomistic simulations is the availability of a high quality potential energy surface and methods to explore it efficiently. Potential energy surfaces calculated on the density functional levelare usually considered to be state of the art, even though their accuracy is not sufficient in numerous cases. In this project several key aspects of atomistic simulations will be addressed. Based on our recently developed methods to navigate in the configurational space, the efficiency of our structure prediction schemes will be further improved and its applicability enlarged. In addition we will deducefrom our exploration of the potential energy surface not only structural but also dynamic properties. Improved density functional methods will be implemented to give higher accuracy potential energy surfaces and consequently improved predictability for atomistic simulations. We will work both on the validation of methods within mainstream density functional schemes as well as on some non-standard approaches inspired by quantum chemistry methods. For some specific systems, machine learning based force fields will be constructed that are not only highly accurate but also orders of magnitude faster to evaluate than potential energy surfaces resulting from density functional calculations. All these developments will allow to find new materials with useful properties faster and to predict their properties with higher reliability. In particular we will apply these methods to study molecular crystals and cluster assembled materials.