Evidence map›Paper›PMID 40020187›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Defects induce phase transition from dynamic to static rippling in graphene.

Fabian L Thiemann, Camille Scalliet, Erich A Müller, Angelos Michaelides

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Modulating Roughness and Ripple Dynamics in Hexagonal Boron Nitride via Defect Engineering.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026
    Article
  2. Article
  3. Defects induce phase transition from dynamic to static rippling in graphene.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Fabian L ThiemannIBM Research Europe, Daresbury WA4 4AD, United Kingdom.ORCID 0000-0003-2951-6740
Camille ScallietLaboratoire de Physique de l'Ecole Normale Supérieure, École Normale Supérieure, Université Paris Sciences et Lettres, CNRS, Sorbonne Université, Université de Paris, Paris F-75005, France.ORCID 0000-0002-7969-891X
Erich A MüllerDepartment of Chemical Engineering, Sargent Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, United Kingdom.ORCID 0000-0002-1513-6686
Angelos MichaelidesYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.ORCID 0000-0002-9169-169X

Funding

EC | European Research Council (ERC) 101071937UKRI | Engineering and Physical Sciences Research Council (EPSRC) EP/L000202UKRI | Engineering and Physical Sciences Research Council (EPSRC) EP/P020194/1UKRI | Engineering and Physical Sciences Research Council (EPSRC) EP/R029431UKRI | Engineering and Physical Sciences Research Council (EPSRC) EP/T022213/1
6 · The paper itself

Abstract

Two-dimensional (2D) materials display nanoscale dynamic ripples that significantly impact their properties. Defects within the crystal lattice are the elementary building blocks to tailor the material's morphology. While some studies have explored the link between defective structures and rippling dynamics in 2D materials, a comprehensive understanding of this relationship has yet to be achieved. Here, we address this using machine learning-driven molecular dynamics simulations. Specifically, we find that above a critical concentration of defects, free-standing graphene sheets undergo a dynamic transition from freely propagating to static ripples. Our computational approach captures the dynamics with atomic resolution, and reveals that the transition is driven by elastic interactions between defects. The strength of these interactions is found to vary across defect types and we identify a unifying set of principles driving the dynamic-to-static transition in 2D materials. Our work not only rationalizes puzzling experimental results for defective 2D materials, but also paves the way to design two-dimensional devices with tailored rippling dynamics. These insights could lay the foundations for a class of disorder-based catalytic and interfacial materials.

Indexed as

defectsgraphenemachine learning potentialtwo-dimensional materials

Identifiers

PMID40020187
PMCPMC11892612

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.