Publications

Here we collect publications that use/discuss/cite httk:

  1. Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design, Armiento R. (2020) Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design. In: Schütt K., Chmiela S., von Lilienfeld O., Tkatchenko A., Tsuda K., Müller KR. (eds) Machine Learning Meets Quantum Physics. Lecture Notes in Physics, vol 968. Springer, Cham. https://doi.org/10.1007/978-3-030-40245-7_17 (2020).
  2. ADAQ: Automatic workflows for magneto-optical properties of point defects in semiconductors, J Davidsson, V Ivády, R Armiento, IA Abrikosov, arXiv preprint arXiv:2008.12539 (2020).
  3. Theoretical study of the phase transitions and electronic structure of (Zr 0.5, Mg 0.5) N and (Hf 0.5, Mg 0.5) N, MA Gharavi, R Armiento, B Alling, P Eklund Journal of Materials Science, 1-8 (2020).
  4. Identification of divacancy and silicon vacancy qubits in 6H-SiC, Joel Davidsson, Viktor Ivády, Rickard Armiento, Takeshi Ohshima, NT Son, Adam Gali, Igor A Abrikosov, Applied Physics Letters 114 (11), 112107 (2019).
  5. Theoretical study of phase stability, crystal and electronic structure of MeMgN2 (Me = Ti, Zr, Hf) compounds, MA Gharavi, R Armiento, B Alling, P Eklund, Journal of materials science 53 (6), 4294-4305 (2018).
  6. First principles predictions of magneto-optical data for semiconductor point defect identification: the case of divacancy defects in 4H–SiC, J Davidsson, V Ivády, R Armiento, NT Son, A Gali, IA Abrikosov New Journal of Physics 20 (2), 023035 (2018).
  7. Machine Learning Energies of 2 M Elpasolite (ABC2D6) Crystals, FA Faber, A Lindmaa, OA Von Lilienfeld, R Armiento, Physical review letters 117 (13), 135502 (2016).
  8. Strong piezoelectric response in stable TiZnN2, ZrZnN2, and HfZnN2 found by ab initio high-throughput approach, C Tholander, CBA Andersson, R Armiento, F Tasnadi, B Alling Journal of Applied Physics 120 (22), 225102 (2016).
  9. Crystal structure representations for machine learning models of formation energies, F Faber, A Lindmaa, OA von Lilienfeld, R Armiento, International Journal of Quantum Chemistry 115 (16), 1094-1101 (2015).