Design Rules for Two‐Dimensional Organic Semiconductor‐Incorporated Perovskites (OSiP) Gleaned from Thousands of Simulated Structures

• Published in: Angewandte Chemie (International ed.) 2023-08, Vol.62 (33), p.e202305298-n/a
• Main Authors: Lin, Zih‐Yu, Sun, Jiaonan, Shiring, Stephen B., Dou, Letian, Savoie, Brett M.
• Format: Article
• Language: English
• Subjects: 2D Organic-Inorganic Perovskites; Design; High-Throughput Framework; Ligands; Machine learning; Molecular Dynamics; Optoelectronics; Perovskites; Simulation; Stability Prediction; Structural stability
• ISSN: 1433-7851; 1521-3773
• DOI: 10.1002/anie.202305298

Two‐dimensional (2D) halide perovskites are an attractive class of hybrid perovskites that have additional optoelectronic tunability due to their accommodation of relatively large organic ligands. Nevertheless, contemporary ligand design depends on either expensive trial‐and‐error testing of whether a ligand can be integrated within the lattice or conservative heuristics that unduly limit the scope of ligand chemistries. Here, the structural determinants of stable ligand incorporation within Ruddlesden‐Popper (RP) phase perovskites are established by molecular dynamics (MD) simulations of over ten‐thousand RP‐phase perovskites and training of machine learning classifiers capable of predicting structural stability based solely on generalizable ligand features. The simulation results show near‐perfect predictions of positive and negative literature examples, predict trade‐offs between several ligand features and stability, and ultimately predict an inexhaustibly large 2D‐compatible ligand design‐space. 2D halide perovskites can accommodate a vast array of organic semiconducting ligands to potentially augment their functionality. A library of over 104 perovskites have been characterized by molecular dynamics and used to train machine learning models.