IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-22437-0.html
   My bibliography  Save this article

Bias free multiobjective active learning for materials design and discovery

Author

Listed:
  • Kevin Maik Jablonka

    (Ecole Polytechnique Fédérale de Lausanne (EPFL))

  • Giriprasad Melpatti Jothiappan

    (BASF Corporation)

  • Shefang Wang

    (BASF Corporation)

  • Berend Smit

    (Ecole Polytechnique Fédérale de Lausanne (EPFL))

  • Brian Yoo

    (BASF Corporation)

Abstract

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.

Suggested Citation

  • Kevin Maik Jablonka & Giriprasad Melpatti Jothiappan & Shefang Wang & Berend Smit & Brian Yoo, 2021. "Bias free multiobjective active learning for materials design and discovery," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22437-0
    DOI: 10.1038/s41467-021-22437-0
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-22437-0
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-22437-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tianyu Wu & Min Zhou & Jingcheng Zou & Qi Chen & Feng Qian & Jürgen Kurths & Runhui Liu & Yang Tang, 2024. "AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    2. Samantha M. McDonald & Emily K. Augustine & Quinn Lanners & Cynthia Rudin & L. Catherine Brinson & Matthew L. Becker, 2023. "Applied machine learning as a driver for polymeric biomaterials design," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22437-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.