Evidence map›Paper›PMID 38773093›Full record

ArticleNature communications2024

Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership.

Kelsey L Snapp, Benjamin Verdier, Aldair E Gongora, Samuel Silverman, Adedire D Adesiji, Elise F Morgan, Timothy J Lawton, Emily Whiting, Keith A Brown

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
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  5. Review
  6. Article
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  10. 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

9 authors.

Kelsey L SnappDepartment of Mechanical Engineering, Boston University, Boston, MA, USA.ORCID http://orcid.org/0000-0001-5984-0723
Benjamin VerdierDepartment of Computer Science, Boston University, Boston, MA, USA.
Aldair E GongoraDepartment of Mechanical Engineering, Boston University, Boston, MA, USA.ORCID http://orcid.org/0000-0002-1287-1570
Samuel SilvermanDepartment of Computer Science, Boston University, Boston, MA, USA.ORCID http://orcid.org/0009-0009-7585-6106
Adedire D AdesijiDepartment of Mechanical Engineering, Boston University, Boston, MA, USA.ORCID http://orcid.org/0000-0001-8973-5748
Elise F MorganDepartment of Mechanical Engineering, Boston University, Boston, MA, USA.ORCID http://orcid.org/0000-0002-3994-1890
Timothy J LawtonSoldier Protection Directorate, US Army Combat Capabilities Development Command Soldier Center, Natick, MA, USA.ORCID http://orcid.org/0000-0002-5820-2153
Emily WhitingDepartment of Computer Science, Boston University, Boston, MA, USA.
Keith A BrownDepartment of Mechanical Engineering, Boston University, Boston, MA, USA. brownka@bu.edu.ORCID http://orcid.org/0000-0002-2379-2018

Funding

Honeywell Federal Manufacturing and Technologies (Honeywell Federal Manufacturing & Technologies) N000471618
6 · The paper itself

Abstract

Energy absorbing efficiency is a key determinant of a structure's ability to provide mechanical protection and is defined by the amount of energy that can be absorbed prior to stresses increasing to a level that damages the system to be protected. Here, we explore the energy absorbing efficiency of additively manufactured polymer structures by using a self-driving lab (SDL) to perform >25,000 physical experiments on generalized cylindrical shells. We use a human-SDL collaborative approach where experiments are selected from over trillions of candidates in an 11-dimensional parameter space using Bayesian optimization and then automatically performed while the human team monitors progress to periodically modify aspects of the system. The result of this human-SDL campaign is the discovery of a structure with a 75.2% energy absorbing efficiency and a library of experimental data that reveals transferable principles for designing tough structures.

Identifiers

PMID38773093
PMCPMC11109101

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.