ArticleAngewandte Chemie (International ed. in English)2024
Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide.
Article in Angewandte Chemie (International ed. in English), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Medium-Range Structural Order in Amorphous Arsenic.Journal of the American Chemical Society · 2026Article
- Methodological Frameworks for Computational Electrocatalysis: From Theory to Practice.Small methods · 2026Review
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential.Nature communications · 2025Article
- Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide.Angewandte Chemie (International ed. in English) · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Graphene oxide (GO) materials are widely studied, and yet their atomic-scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine-learning methods, combining on-the-fly acceleration for first-principles molecular dynamics with message-passing neural-network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state-of-the-art accuracy and predictive power. We apply the method to the thermal reduction of GO, which we describe in a realistic (ten-nanometre scale) structural model. Our simulations are consistent with recent experimental findings, including X-ray photoelectron spectroscopy (XPS), and help to rationalise them in atomistic and mechanistic detail. More generally, our work provides a platform for routine, accurate, and predictive simulations of diverse carbonaceous materials.
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Registered trials
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