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- W4386690908 abstract "Abstract High‐throughput computational screening and machine learning hold significant potential for exploring diverse chemical compositions and discovering novel inorganic solids. However, the complexity of point defects, which occur in all inorganic solids and are often crucial to their functionality and synthesizability, presents significant challenges. Here, this study presents a defect‐chemistry‐trained, interpretable machine learning approach, designed to accelerate the exploration and discovery of unconventional proton‐conducting inorganic solid electrolytes. By considering dopant dissolution and hydration reactions, the machine learning models provide quantitative predictions and physical interpretations for synthesizable host–dopant combinations with hydration capabilities across various structures. Utilizing these insights, two unconventional proton conductors, Pb‐doped Bi 12 SiO 20 sillenite and eulytite‐type Sr‐doped Bi 4 Ge 3 O 12 , are discovered in the first two synthesis trials. Notably, the Pb‐doped Bi 12 SiO 20 represents an unprecedented class of proton‐conducting electrolyte composed solely of groups 14 and 15 cations and featuring a sillenite structure. It exhibits unique and fast 3D proton conduction along a loosely bonded BiO 5 network. This study demonstrates an efficient approach for exploring novel inorganic materials." @default.
- W4386690908 created "2023-09-13" @default.
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- W4386690908 date "2023-09-11" @default.
- W4386690908 modified "2023-10-18" @default.
- W4386690908 title "Discovery of Unconventional Proton‐Conducting Inorganic Solids via Defect‐Chemistry‐Trained, Interpretable Machine Learning" @default.
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- W4386690908 doi "https://doi.org/10.1002/aenm.202301892" @default.
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