ALT: Asterix Drinks Magic Potion

The Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences is deeply saddened to announce that on Thursday 23 July 2026, our outstanding colleague, teacher, mentor and…

We investigate MACE-MP-0 and M3GNet, two general-purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials....

Title: Assistant Professor in Chemistry x2 (111367-0226). Application Deadline: . Position Type: Permanent

Two-dimensional (2D) hybrid perovskite semiconductors with nonprimary ammonium cations (NPACs) have recently attracted interest for spin-optoelectronics owing to the symmetry-breaking distortions in their crystal structures. However, implementing this design strategy in three-dimensional (3D) analogs remains largely unexplored, primarily due to stricter restrictions on cation size. Here, we introduce a family of 3D hybrid perovskites (3DHPs), ASnX3, where A = 3-hydroxyazetidinium (AzOH; +NH2(CH2)2CHOH) and X = Cl, Br, and I. The choice of a bulky NPAC, coupled with the incorporation of a polar group (−OH), targets broken symmetry within the 3D frameworks. While single-crystal X-ray diffraction analysis reveals spatially averaged centrosymmetric cubic (i.e., Pm-3m space group) unit cells, with the largest lattice parameters among existing ASnX3 analogs, first-principles molecular dynamics and electronic structure calculations indicate that the relatively fixed dipoles of AzOH within the SnX6-derived framework introduce local inversion asymmetry and spin polarization. By incorporating a polar cation with limited mobility into the 3D perovskite framework, (AzOH)SnX3 unlocks potential for spin-optoelectronics, photovoltaics, ferroelectrics, and nonlinear optics.

Fixed-Term Research Associate (m/f/d, E13 TV-L, 67%) in the field of Machine Learning for Materials Chemistry

Hybrid organic–inorganic (HOI) antimony and bismuth halides exhibit diverse structural features and have been studied intensely for their promising electronic and optical properties. There are well-explored structure–property relations for these materials. However, a thorough understanding of the synthesis routes and templating effects is lacking, turning their targeted synthesis into an open challenge. In this study, we assemble a literature data set of established HOI material candidates and train an explainable machine learning classification model to explore the templating effects in more detail. With a classification accuracy upward of 70%, our model is effective in predicting HOI structure types based on the reactants and points out several structural and electrostatic design features for the organic cation that influence the inorganic substructure most strongly. We further demonstrate the validity of our classifier on 9 newly synthesized members of this materials class and propose incremental learning routes to expand the model in future research.