Abu-Farsakh, H.; Neugebauer, J.: Enhancing nitrogen solubility in GaAs and InAs by surface kinetics: An ab initio study. Physical Review B 79, 155311, pp. 155311 - 155323 (2009)
Abu-Farsakh, H.; Neugebauer, J.: Exploring the unusual diffusion of N adatoms on GaAs(001) using first principles calculations. DPG Frühjahrstagung 2010, Regensburg, Germany (2010)
Abu-Farsakh, H.; Neugebauer, J.: Exploring the unusual diffusion of N adatoms at GaAs(001) surface. Computational Materials Science on Complex Energy Landscapes Workshop, Imst, Austria (2010)
Abu-Farsakh, H.; Neugebauer, J.: Enhancing N solubility in diluted nitrides by surface kinetics: An ab-initio study. Spring meeting of the German Physical Society (DPG), Berlin, Germany (2008)
Abu-Farsakh, H.; Neugebauer, J.: Ab-initio study of the thermodynamics and kinetics of N at GaAs(001) surface. PAW workshop 2007, Goslar, Germany (2007)
Abu-Farsakh, H.; Neugebauer, J.: In-N anti-correlation in InGaAsN alloys: The delicate interplay between adatom thermodynamics and kinetics. Spring meeting of the German Physical Society (DPG), Regensburg, Germany (2007)
Abu-Farsakh, H.; Neugebauer, J.: Tailoring the N-solubility in InGaAs-alloys by surface engineering: Applications and limits. 1. Harzer Ab initio Workshop, Clausthal, Germany (2006)
Abu-Farsakh, H.; Neugebauer, J.: Incorporation of N at GaAs and InAs surfaces: An ab-initio study. Technische Universität Berlin, Berlin, Germany (2006)
Abu-Farsakh, H.; Dick, A.; Neugebauer, J.: Incorporation of N at GaAs and InAs surfaces. Deutsche Physikalische Gesellschaft Spring Meeting of the Division Condensed Matter, Dresden, Germany (2006)
Abu-Farsakh, H.; Neugebauer, J.: Combined ab-initio and Monte Carlo calculations to explore the surface thermodynamics and kinetics of dilute nitrides. 8th International Conference on Nitride Semiconductors (ICNS-8), Jeju Island, South Korea (2009)
Abu-Farsakh, H.; Neugebauer, J.: The role of surface kinetics in achieving high non-equilibrium N concentrations in bulk GaAs. DPG Spring Meeting 2009, Dresden, Germany (2009)
Abu-Farsakh, H.; Neugebauer, J.; Albrecht, M.: Ab-initio study of compositional anti-correlation of In and N in InGaAsN alloys. The 7th International Conference of Nitride Semiconductors (ICNS-7), Las Vegas, NV, USA (2007)
Abu-Farsakh, H.; Neugebauer, J.: Enhancing the solubility of N in GaAs and InAs by surface kinetics. 28th International Conference on the Physics of Semiconductors, Vienna, Austria (2006)
Abu-Farsakh, H.; Neugebauer, J.: Enhancing bulk solubility by surface engineering: An ab-initio study. Workshop: Ab initio Description of Iron and Steel, Status and future challenges, Ringberg Castle, Germany (2006)
Abu-Farsakh, H.: Understanding the interplay between thermodynamics and surface kinetics in the growth of dilute nitride alloys from first principles. Dissertation, University of Paderborn, Paderborn, Germany (2010)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.