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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01676-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849434131502465024 |
|---|---|
| author | Simon Gramatte Olivier Politano Noel Jakse Claudia Cancellieri Ivo Utke Lars P. H. Jeurgens Vladyslav Turlo |
| author_facet | Simon Gramatte Olivier Politano Noel Jakse Claudia Cancellieri Ivo Utke Lars P. H. Jeurgens Vladyslav Turlo |
| author_sort | Simon Gramatte |
| collection | DOAJ |
| description | 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 deposited alumina, a fast atomistic simulation technique is introduced using an ab initio-based machine learning interatomic potential to generate amorphous structures with realistic hydrogen contents. As such, the annealing of highly defective crystalline hydroxide structures at atomic layer deposition temperatures reproduces experimental density and structure, enabling accurate prediction of Al Auger parameter chemical shifts. Our analysis shows that higher hydrogen content favors OH ligands, whereas lower hydrogen content leads to diverse chemical states and hydrogen bonding, consistent with charge density and partial Bader charge calculations. Our approach offers a robust route to link hydrogen content with experimentally accessible chemical shifts, aiding the design of next-generation hydrogen-related materials. |
| format | Article |
| id | doaj-art-50e37e268b9b457fb3d6a62d74ea8c0c |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-50e37e268b9b457fb3d6a62d74ea8c0c2025-08-20T03:26:47ZengNature Portfolionpj Computational Materials2057-39602025-06-0111111310.1038/s41524-025-01676-5Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modelingSimon Gramatte0Olivier Politano1Noel Jakse2Claudia Cancellieri3Ivo Utke4Lars P. H. Jeurgens5Vladyslav Turlo6Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and TechnologyLaboratoire Interdisciplinaire Carnot de Bourgogne ICB UMR 6303, Université Bourgogne Europe, CNRSGrenoble-INP, SIMaP, Université Grenoble Alpes, CNRSLaboratory for Joining Technologies and Corrosion, Empa - Swiss Federal Laboratories for Materials Science and TechnologyLaboratory for Mechanics of Materials and Nanostructures, Empa - Swiss Federal Laboratories for Materials Science and TechnologyLaboratory for Joining Technologies and Corrosion, Empa - Swiss Federal Laboratories for Materials Science and TechnologyLaboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and TechnologyAbstract 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 deposited alumina, a fast atomistic simulation technique is introduced using an ab initio-based machine learning interatomic potential to generate amorphous structures with realistic hydrogen contents. As such, the annealing of highly defective crystalline hydroxide structures at atomic layer deposition temperatures reproduces experimental density and structure, enabling accurate prediction of Al Auger parameter chemical shifts. Our analysis shows that higher hydrogen content favors OH ligands, whereas lower hydrogen content leads to diverse chemical states and hydrogen bonding, consistent with charge density and partial Bader charge calculations. Our approach offers a robust route to link hydrogen content with experimentally accessible chemical shifts, aiding the design of next-generation hydrogen-related materials.https://doi.org/10.1038/s41524-025-01676-5 |
| spellingShingle | Simon Gramatte Olivier Politano Noel Jakse Claudia Cancellieri Ivo Utke Lars P. H. Jeurgens Vladyslav Turlo Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling npj Computational Materials |
| title | Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling |
| title_full | Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling |
| title_fullStr | Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling |
| title_full_unstemmed | Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling |
| title_short | Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling |
| title_sort | unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning driven atomistic modeling |
| url | https://doi.org/10.1038/s41524-025-01676-5 |
| work_keys_str_mv | AT simongramatte unveilinghydrogenchemicalstatesinsupersaturatedamorphousaluminaviamachinelearningdrivenatomisticmodeling AT olivierpolitano unveilinghydrogenchemicalstatesinsupersaturatedamorphousaluminaviamachinelearningdrivenatomisticmodeling AT noeljakse unveilinghydrogenchemicalstatesinsupersaturatedamorphousaluminaviamachinelearningdrivenatomisticmodeling AT claudiacancellieri unveilinghydrogenchemicalstatesinsupersaturatedamorphousaluminaviamachinelearningdrivenatomisticmodeling AT ivoutke unveilinghydrogenchemicalstatesinsupersaturatedamorphousaluminaviamachinelearningdrivenatomisticmodeling AT larsphjeurgens unveilinghydrogenchemicalstatesinsupersaturatedamorphousaluminaviamachinelearningdrivenatomisticmodeling AT vladyslavturlo unveilinghydrogenchemicalstatesinsupersaturatedamorphousaluminaviamachinelearningdrivenatomisticmodeling |