Data-Driven Mapping of the Cesium Cadmium Bromide Phase Space Utilizing a Soft-Chemistry Approach
Soft-chemistry techniques provide a versatile approach to synthesizing inorganic materials under mild conditions, enabling access to compositions and structures that are challenging to achieve through traditional thermodynamically driven solid-state methods. However, these solution-based routes often result in phase competition, requiring precise control over reaction conditions to achieve selective product formation. While one-variable-at-a-time (OVAT) approaches have traditionally been used for phase selection, data-driven strategies are emerging as more efficient methods for navigating complex synthetic spaces. Ternary metal halides, such as cesium cadmium bromides (Cs-Cd-Br), are of growing interest due to their potential in wide and ultrawide band gap applications. Unlike the well-studied cesium lead halide phases, the compositional diversity and solution-based synthesis of ternary Cs-Cd-Br phases remain largely unexplored. This study systematically investigates the synthetic phase space of the Cs-Cd-Br system by constructing a data-driven phase map. Using a common set of precursors and a standardized experimental procedure, we successfully synthesize all four known Cs-Cd-Br phases─CsCdBr<sub>3</sub>, Cs<sub>2</sub>CdBr<sub>4</sub>, Cs<sub>3</sub>CdBr<sub>5</sub>, and Cs<sub>7</sub>Cd<sub>3</sub>Br<sub>13</sub>─each exhibiting distinct structures, morphologies, and optical properties. Our findings highlight the potential of soft-chemistry methods for expanding the library of ternary metal halides and provide key insights into the thermodynamic and kinetic factors governing phase formation.
Authors
- Richard L. Brutchey (ORCID: https://orcid.org/0000-0002-7781-5596)
- Kyle D. Crans (ORCID: https://orcid.org/0009-0006-2542-7555)
- Zhaohong Sun (ORCID: https://orcid.org/0000-0002-6176-0308)
- Ariel A. Nehoray
Institutions
- University of Southern California (US)
- California Southern University (US)
Publication Details
- Journal
- Inorganic Chemistry
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1021/acs.inorgchem.5c01137
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52