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.

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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
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article

Data-Driven Mapping of the Cesium Cadmium Bromide Phase Space Utilizing a Soft-Chemistry Approach

Richard L. Brutchey, Kyle D. Crans, Zhaohong Sun, Ariel A. Nehoray
1 citations
Inorganic Chemistry
Machine Learning in Materials Science
0.52
article

Data-Driven Mapping of the Cesium Cadmium Bromide Phase Space Utilizing a Soft-Chemistry Approach

Richard L. Brutchey, Kyle D. Crans, Zhaohong Sun, Ariel A. Nehoray
article en
1 citations

Abstract

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.

Inorganic ChemistryVol. 64(20)
University of Southern California (US), California Southern University (US)
Openalex Percentile: Top 39%
Machine Learning in Materials Science
0.52
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