A Predictive Framework for Discovering Organic Metal Halide Hybrids beyond Perovskites

Hybrid metal halides have transformed optoelectronics,
yet predictive
synthesis remains elusive beyond the perovskite family, to which the
Goldschmidt tolerance factor offers guidance. Here, we present an
open-source platform (https://apollo-omhh.com) that integrates a density functional theory (DFT) database of 7439
organic metal halide hybrids (OMHHs), derived from 118 experimentally
reported compounds spanning diverse electronic structures. Leveraging
this data set and remnant stability theory, we introduce a predictive
framework to assess OMHH synthesizability. This framework successfully
guided the experimental synthesis of eight previously unreported compounds
and achieved an 85.2% prediction accuracy across 27 synthesis attempts.
This framework is further extended to predict 268 synthesizable OMHHs
among 1144 examined OMHHs. Together, these advances establish a data-driven
foundation for accelerating the discovery and rational design of hybrid
materials beyond perovskite structures.