Ektarawong, A.; Simak, S. I.; Hultman, L.; Birch, J.; Tasnádi, F.; Wang, F.; Alling, B.: Effects of configurational disorder on the elastic properties of icosahedral boron-rich alloys based on B6O, B13C2, and B4C, and their mixing thermodynamics. The Journal of Chemical Physics 144 (13), 134503 (2016)
Sangiovanni, D. G.; Hellman, O.; Alling, B.; Abrikosov, I. A.: Efficient and accurate determination of lattice-vacancy diffusion coefficients via non equilibrium ab initio molecular dynamics. Physical Review B 93 (9), 094305 (2016)
Thore, A.; Dahlqvist, M.; Alling, B.; Rosén, J. A.: Magnetic exchange interactions and critical temperature of the nanolaminate Mn2GaC from first-principles supercell methods. Physical Review B 93 (5), 054432 (2016)
Eklund, P.; Kerdsongpanya, S.; Alling, B.: Transition-metal-nitride-based thin films as novel energy harvesting materials. Journal of Materials Chemistry C 4 (18), pp. 3905 - 3914 (2016)
Thore, A.; Dahlqvist, M.; Alling, B.; Rosén, J. A.: Phase stability of the nanolaminates V2Ga2C and (Mo1-xVx)2Ga2C from first-principles calculations. Physical Chemistry Chemical Physics 18 (18), pp. 12682 - 12688 (2016)
Stockem, I.; Alling, B.: Interdependence of the spin and lattice dynamics of CrN in the high temperature paramagnetic phase. DPG Frühjahrstagung, Dresden, Germany (2017)
Dutta, B.; Körmann, F.; Alling, B.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Interaction of magnetic and lattice degrees of freedom. International Workshop on Ab initio Description of Iron and Steel: Mechanical Properties (ADIS 2016), Ringberg Castle, Tegernsee, Germany (2016)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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.