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Autonomous experimentation systems for materials development: A community perspective

  • Eric Stach
  • , Brian DeCost
  • , A. Gilad Kusne
  • , Jason Hattrick-Simpers
  • , Keith A. Brown
  • , Kristofer G. Reyes
  • , Joshua Schrier
  • , Simon Billinge
  • , Tonio Buonassisi
  • , Ian Foster
  • , Carla P. Gomes
  • , John M. Gregoire
  • , Apurva Mehta
  • , Joseph Montoya
  • , Elsa Olivetti
  • , Chiwoo Park
  • , Eli Rotenberg
  • , Semion K. Saikin
  • , Sylvia Smullin
  • , Valentin Stanev
  • Benji Maruyama
  • University of Pennsylvania
  • National Institute of Standards and Technology
  • University of Maryland, College Park
  • Boston University
  • Fordham University
  • Columbia University
  • Brookhaven National Laboratory Condensed Matter Physics and Materials Science Department
  • Massachusetts Institute of Technology
  • Argonne National Laboratory
  • The University of Chicago
  • Cornell University
  • California Institute of Technology
  • MS 69
  • Toyota Research Institute
  • Florida State University
  • United States Department of Energy
  • Kebotix, Inc.
  • Form Energy, Inc.
  • Air Force Research Laboratory

Research output: Contribution to journalReview articlepeer-review

311 Scopus citations

Abstract

Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

Original languageEnglish
Pages (from-to)2702-2726
Number of pages25
JournalMatter
Volume4
Issue number9
DOIs
StatePublished - Sep 1 2021

Keywords

  • additive manufacturing
  • algorithmic development
  • artificial intelligence
  • autonomy
  • carbon nanotubes
  • human-machine teaming
  • machine learning
  • materials discovery
  • research methods
  • workforce development

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