Predictive Design of Novel Two-Dimensional Materials

The predictive power of physical theories has led to remarkable findings such as the discovery of planets,
new elementary particles, and gravitational waves. The Dresden-concept group “Autonomous Materials
Thermodynamics – AutoMaT” leverages ab initio density functional theory as a predictive tool for
materials design. We specifically focus on the data-driven discovery of novel two-dimensional (2D)
materials for future electronics and energy applications with strong partners from HZDR,
Forschungszentrum Jülich, the DFG collaborative research center “Synthetic Two-dimensional
Materials” hosted at TU Dresden, and Duke University (United States).
Two-dimensional (2D) materials are traditionally derived from bulk layered compounds. The recent
surprising experimental realization of some 2D sheets obtained from non-layered crystals [1,2]
foreshadows a new direction for this diverse class of nanostructures. Generalizing these findings, we
recently predicted by data-driven methods and autonomous ab initio calculations a large set of novel
representatives [3,4] (see Figure 1). They exhibit diverse magnetic properties such as complex surface
spin polarizations enabling spintronics. These systems are thus an attractive platform for fundamental and
applied nanoscience.
[1] A. Puthirath Balan et al., Nat. Nanotechnol. 13, 602 (2018).
[2] A. Puthirath Balan et al., Chem. Mater. 30, 5923 (2018).
[3] R. Friedrich et al., Nano Lett. 22, 989 (2022).
[4] T. Barnowsky et al., Adv. Electron. Mater. 2201112 (2023).