Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions

Exploratory synthesis of solids is essential for the advancement of materials science but is also highly time- and resource-intensive. Here, we demonstrate an efficient strategy to explore solid-state synthesis of quaternary cesium chlorides in the search space of Cs<sub><i>n</i></sub>A<sup>I</sup>BCl<sub>6</sub> (<i>n</i> = 2 or 3, A = Li, Na or K, and B = <i>d</i> or <i>p</i>-block metal), where the target compositions are selected from a pool of candidates based on computationally predicted stabilities and availability of viable precursor powders. Synthesizability of the targets is assessed by observing the evolution of starting phases upon heating under in situ synchrotron X-ray diffraction. Laboratory synthesis is attempted for promising targets, and resulting materials are characterized by powder X-ray and neutron diffraction and subsequent Rietveld refinement. We focus on how computational predictions can be bridged to experimental characterizations in exploratory synthesis and report on successful and failed synthesis attempts for compounds of type Cs<sub>2</sub>A<sup>I</sup>B<sup>III</sup>Cl<sub>6</sub>, revealing underexplored variants including new polymorphs of Cs<sub>2</sub>LiCrCl<sub>6</sub> and Cs<sub>2</sub>LiRuCl<sub>6</sub>, and a new compound Cs<sub>2</sub>LiIrCl<sub>6</sub>.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2024-10-16
DOI
https://doi.org/10.1021/jacs.4c10294
Citations
4
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.47

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article

Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions

Muratahan Aykol, Pushmeet Kohli, Shintaro Kobayashi, Simon Batzner et al.
4 citations
Journal of the American Chemical Society
Machine Learning in Materials Science
0.47
article

Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions

Muratahan Aykol, Pushmeet Kohli, Shintaro Kobayashi, Simon Batzner, Amil Merchant, AKIRA B. MIURA, Chikako Moriyoshi, Shogo Kawaguchi, Ekin D. Cubuk, Hiroshi Kageyama, Kiyoharu Tadanaga, Chul‐Ho Lee, Yongming Wang, S Kozaki
article en
4 citations

Abstract

Exploratory synthesis of solids is essential for the advancement of materials science but is also highly time- and resource-intensive. Here, we demonstrate an efficient strategy to explore solid-state synthesis of quaternary cesium chlorides in the search space of Cs<sub><i>n</i></sub>A<sup>I</sup>BCl<sub>6</sub> (<i>n</i> = 2 or 3, A = Li, Na or K, and B = <i>d</i> or <i>p</i>-block metal), where the target compositions are selected from a pool of candidates based on computationally predicted stabilities and availability of viable precursor powders. Synthesizability of the targets is assessed by observing the evolution of starting phases upon heating under in situ synchrotron X-ray diffraction. Laboratory synthesis is attempted for promising targets, and resulting materials are characterized by powder X-ray and neutron diffraction and subsequent Rietveld refinement. We focus on how computational predictions can be bridged to experimental characterizations in exploratory synthesis and report on successful and failed synthesis attempts for compounds of type Cs<sub>2</sub>A<sup>I</sup>B<sup>III</sup>Cl<sub>6</sub>, revealing underexplored variants including new polymorphs of Cs<sub>2</sub>LiCrCl<sub>6</sub> and Cs<sub>2</sub>LiRuCl<sub>6</sub>, and a new compound Cs<sub>2</sub>LiIrCl<sub>6</sub>.

Journal of the American Chemical SocietyVol. 146(43)
Hiroshima University (JP), Google (United States) (US), Hokkaido University (JP), Kyoto University (JP), Google DeepMind (United Kingdom) (GB), Japan Synchrotron Radiation Research Institute (JP), National Institute of Advanced Industrial Science and Technology (JP)
Japan Science and Technology Corporation, Japan Society for the Promotion of Science, Institute for Materials Research, Tohoku University, Division of Materials Research
Openalex Percentile: Top 38%
Machine Learning in Materials Science
0.47
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