Insights into the Structure and Dynamics of Water at Co$_3$O$_4$(001) Using a High-Dimensional Neural Network Potential
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911130451968000 |
|---|---|
| author | Omranpour, Amir Behler, Jörg |
| author_facet | Omranpour, Amir Behler, Jörg |
| contents | Co$_3$O$_4$ is an important catalyst for the oxidation of organic molecules in the liquid phase. Still, understanding the atomistic details of Co$_3$O$_4$-water interfaces under operando conditions remains extremely challenging. While ab initio molecular dynamics have become an essential tool for investigating these dynamic interfaces in silico, they are limited to only a few picoseconds and a few hundred atoms. In this work, we overcome these limitations by training a high-dimensional neural network potential (HDNNP) on density functional theory data, which allows us to significantly extend the accessible time and length scales. Employing this HDNNP, we perform simulations to unravel the structure, dynamics, and reactivity of Co$_3$O$_4$(001)-water interfaces in detail. Our simulations reveal distinct characteristics of the two possible A and B terminations. The B-terminated surface stabilizes a compact, quasi-epitaxial hydration layer with strong templating effects, enhanced hydroxylation, and a well-organized hydrogen-bond network. In contrast, the A-termination forms a more diffuse contact layer with weaker templating, lower hydroxylation, and less ordered interfacial water. Extended simulations further uncover proton transfer pathways, including intermittent protonation of surface hydroxyls, migration of water molecules into the epitaxial layer, and rare hydronium-like configurations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00322 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Insights into the Structure and Dynamics of Water at Co$_3$O$_4$(001) Using a High-Dimensional Neural Network Potential Omranpour, Amir Behler, Jörg Chemical Physics Materials Science Co$_3$O$_4$ is an important catalyst for the oxidation of organic molecules in the liquid phase. Still, understanding the atomistic details of Co$_3$O$_4$-water interfaces under operando conditions remains extremely challenging. While ab initio molecular dynamics have become an essential tool for investigating these dynamic interfaces in silico, they are limited to only a few picoseconds and a few hundred atoms. In this work, we overcome these limitations by training a high-dimensional neural network potential (HDNNP) on density functional theory data, which allows us to significantly extend the accessible time and length scales. Employing this HDNNP, we perform simulations to unravel the structure, dynamics, and reactivity of Co$_3$O$_4$(001)-water interfaces in detail. Our simulations reveal distinct characteristics of the two possible A and B terminations. The B-terminated surface stabilizes a compact, quasi-epitaxial hydration layer with strong templating effects, enhanced hydroxylation, and a well-organized hydrogen-bond network. In contrast, the A-termination forms a more diffuse contact layer with weaker templating, lower hydroxylation, and less ordered interfacial water. Extended simulations further uncover proton transfer pathways, including intermittent protonation of surface hydroxyls, migration of water molecules into the epitaxial layer, and rare hydronium-like configurations. |
| title | Insights into the Structure and Dynamics of Water at Co$_3$O$_4$(001) Using a High-Dimensional Neural Network Potential |
| topic | Chemical Physics Materials Science |
| url | https://arxiv.org/abs/2509.00322 |