Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling
Abstract Advancing hydrogen-based technologies requires detailed characterization of hydrogen chemical states in amorphous materials. As experimental probing of hydrogen is challenging, interpretation in amorphous systems demands accurate structural models. Guided by experiments on atomic layer depo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01676-5 |
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