Evidence map›Paper›PMID 41741734›Full record

ArticleNature materials2026

Topological control of spontaneous failure in active nematic solids.

Sheng Chen, Matthew Ricci, A Pasha Tabatabai, Zachary Gao Sun, Sven Witthaus, Suraj Shankar, Mor Nitzan, Michael P Murrell

Abstract read
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In one paragraph

Article in Nature materials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

8 authors.

Sheng ChenDepartment of Biomedical Engineering, Yale University, New Haven, CT, USA. sheng.chen.sc2959@yale.edu.ORCID http://orcid.org/0000-0002-8325-4481
Matthew RicciSchool of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
A Pasha TabatabaiDepartment of Biomedical Engineering, Yale University, New Haven, CT, USA.
Zachary Gao SunSystems Biology Institute, Yale University, West Haven, CT, USA.ORCID http://orcid.org/0000-0003-3658-109X
Sven WitthausSystems Biology Institute, Yale University, West Haven, CT, USA.
Suraj ShankarDepartment of Physics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-4615-975X
Mor NitzanSchool of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
Michael P MurrellDepartment of Biomedical Engineering, Yale University, New Haven, CT, USA. michael.murrell@yale.edu.ORCID http://orcid.org/0000-0001-6788-2077

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Active solids using energy influx to generate non-equilibrium forces undergo spontaneous mechanical failure, but how topological defects concentrate internal stresses and control breakage in active materials is unknown. Here we assemble a reconstituted two-dimensional actomyosin network that lacks fluidity but exhibits nematic order and network elasticity. Surprisingly, we found that interacting multidefect configurations, especially defect quadrupoles with two +1/2 and two -1/2 defects, play a crucial role. Combining experimental data with an active solid fracture model, we demonstrate that a head quadrupole with mutually facing +1/2 defects can trigger crack opening and material tearing. Meanwhile, tail quadrupoles with mutually opposing +1/2 defects drive transient filament clustering and condenses into asters. We establish a deep learning model to predict the eventual aster formation from the initial topological structures. Our work uncovers a defect-mediated mechanism for spontaneous failure in active solids and provides topological design principles for controlling targeted damage in soft and living systems across scales.

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Registered trials

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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.