ArticleChemical science2022
Exploring the configurational space of amorphous graphene with machine-learned atomic energies.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Shear-Induced Emergence of Aromatic Superlow-Friction Interfaces in Amorphous Carbon: Triggering Chemical Impurities and Atomic-Scale Mechanisms.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Synthesis of Amorphous Graphene and Graphene Oxide Analogues.Journal of the American Chemical Society · 2025Article
- Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics.Nature communications · 2025Article
- Prediction rigidities for data-driven chemistry.Faraday discussions · 2025Article
- Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide.Angewandte Chemie (International ed. in English) · 2024Article
- Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning.Angewandte Chemie (International ed. in English) · 2024Article
- When Machine Learning Meets 2D Materials: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Robustness of Local Predictions in Atomistic Machine Learning Models.Journal of chemical theory and computation · 2023Article
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Authors and funding
3 authors.
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Abstract
Two-dimensionally extended amorphous carbon ("amorphous graphene") is a prototype system for disorder in 2D, showing a rich and complex configurational space that is yet to be fully understood. Here we explore the nature of amorphous graphene with an atomistic machine-learning (ML) model. We create structural models by introducing defects into ordered graphene through Monte-Carlo bond switching, defining acceptance criteria using the machine-learned local, atomic energies associated with a defect, as well as the nearest-neighbor (NN) environments. We find that physically meaningful structural models arise from ML atomic energies in this way, ranging from continuous random networks to paracrystalline structures. Our results show that ML atomic energies can be used to guide Monte-Carlo structural searches in principle, and that their predictions of local stability can be linked to short- and medium-range order in amorphous graphene. We expect that the former point will be relevant more generally to the study of amorphous materials, and that the latter has wider implications for the interpretation of ML potential models.
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