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

Author

Listed:
  • Kelsey L. Snapp

    (Boston University)

  • Benjamin Verdier

    (Boston University)

  • Aldair E. Gongora

    (Boston University)

  • Samuel Silverman

    (Boston University)

  • Adedire D. Adesiji

    (Boston University)

  • Elise F. Morgan

    (Boston University
    Boston University
    Boston University)

  • Timothy J. Lawton

    (US Army Combat Capabilities Development Command Soldier Center)

  • Emily Whiting

    (Boston University)

  • Keith A. Brown

    (Boston University
    Boston University
    Boston University)

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.

Suggested Citation

  • Kelsey L. Snapp & Benjamin Verdier & Aldair E. Gongora & Samuel Silverman & Adedire D. Adesiji & Elise F. Morgan & Timothy J. Lawton & Emily Whiting & Keith A. Brown, 2024. "Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48534-4
    DOI: 10.1038/s41467-024-48534-4
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    References listed on IDEAS

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    1. Benjamin J. Shields & Jason Stevens & Jun Li & Marvin Parasram & Farhan Damani & Jesus I. Martinez Alvarado & Jacob M. Janey & Ryan P. Adams & Abigail G. Doyle, 2021. "Bayesian reaction optimization as a tool for chemical synthesis," Nature, Nature, vol. 590(7844), pages 89-96, February.
    2. Keren J. Kanarik & Wojciech T. Osowiecki & Yu (Joe) Lu & Dipongkar Talukder & Niklas Roschewsky & Sae Na Park & Mattan Kamon & David M. Fried & Richard A. Gottscho, 2023. "Human–machine collaboration for improving semiconductor process development," Nature, Nature, vol. 616(7958), pages 707-711, April.
    3. Hongwei Cheng & Xiaoxia Zhu & Xiaowei Cheng & Pengzhan Cai & Jie Liu & Huijun Yao & Ling Zhang & Jinglai Duan, 2023. "Mechanical metamaterials made of freestanding quasi-BCC nanolattices of gold and copper with ultra-high energy absorption capacity," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
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