fig9

Machine learning-assisted advances and perspectives for electrolytes of protonic solid oxide fuel cells

Figure 9. (A) High-throughput computational workflow for studying proton-conducting materials. Low-energy proton migration pathways and the energy profiles in representative proton-conducting materials; (B) and (C) perovskite YbCoO3; (D) and (E) SrMnO3, (F) and (G) Tb2Mo2O7, (H) and (I) CrMoO4, (J) and (K) MoPO5, (L) and (M) Eu3MoO7. Reproduced with permission from Islam et al.[64] Copyright 2022 American Chemical Society.

Energy Materials
ISSN 2770-5900 (Online)
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