The modus operandi in materials research and development is combining existing data with an understanding of the underlying physics to create and test new hypotheses via experiments or simulations. This process is traditionally driven by subject expertise and the creativity of individual researchers, who “close the loop” by updating their hypotheses and models in light of new data or knowledge acquired from the community. Since the early 2000s, there has been notable progress in the automation of each step of the scientific process. With recent advances in using machine learning for hypothesis generation and artificial intelligence for decision-making, the opportunity to automate the entire closed-loop process has emerged as an exciting research frontier. The future of fully autonomous research systems for materials science no longer feels far-fetched. Autonomous systems are poised to make the search for new materials, properties, or parameters more efficient under budget and time constraints, and in effect accelerate materials innovation. This paper provides a brief overview of closed-loop research systems of today, and our related work at the Toyota Research Institute applied across different materials challenges and identifies both limitations and future opportunities.
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Toward autonomous materials research: Recent progress and future challenges
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March 2022
Research Article|
January 13 2022
Toward autonomous materials research: Recent progress and future challenges

Special Collection:
Autonomous (AI-driven) Materials Science
Joseph H. Montoya
;
Joseph H. Montoya
Toyota Research Institute, Los Altos
, California 94022, USA
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Muratahan Aykol
;
Muratahan Aykol
Toyota Research Institute, Los Altos
, California 94022, USA
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Abraham Anapolsky;
Abraham Anapolsky
Toyota Research Institute, Los Altos
, California 94022, USA
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Chirranjeevi B. Gopal;
Chirranjeevi B. Gopal
Toyota Research Institute, Los Altos
, California 94022, USA
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Patrick K. Herring
;
Patrick K. Herring
Toyota Research Institute, Los Altos
, California 94022, USA
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Jens S. Hummelshøj;
Jens S. Hummelshøj
Toyota Research Institute, Los Altos
, California 94022, USA
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Linda Hung
;
Linda Hung
Toyota Research Institute, Los Altos
, California 94022, USA
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Ha-Kyung Kwon;
Ha-Kyung Kwon
Toyota Research Institute, Los Altos
, California 94022, USA
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Daniel Schweigert;
Daniel Schweigert
Toyota Research Institute, Los Altos
, California 94022, USA
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Shijing Sun
;
Shijing Sun
Toyota Research Institute, Los Altos
, California 94022, USA
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Santosh K. Suram
;
Santosh K. Suram
Toyota Research Institute, Los Altos
, California 94022, USA
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Steven B. Torrisi
;
Steven B. Torrisi
Toyota Research Institute, Los Altos
, California 94022, USA
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Amalie Trewartha
;
Amalie Trewartha
Toyota Research Institute, Los Altos
, California 94022, USA
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Brian D. Storey
Brian D. Storey
a)
Toyota Research Institute, Los Altos
, California 94022, USA
a)Author to whom correspondence should be addressed: brian.storey@tri.global
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a)Author to whom correspondence should be addressed: brian.storey@tri.global
Note: This paper is part of the special collection on Autonomous (AI-driven) Materials Science.
Appl. Phys. Rev. 9, 011405 (2022)
Article history
Received:
October 23 2021
Accepted:
December 21 2021
Citation
Joseph H. Montoya, Muratahan Aykol, Abraham Anapolsky, Chirranjeevi B. Gopal, Patrick K. Herring, Jens S. Hummelshøj, Linda Hung, Ha-Kyung Kwon, Daniel Schweigert, Shijing Sun, Santosh K. Suram, Steven B. Torrisi, Amalie Trewartha, Brian D. Storey; Toward autonomous materials research: Recent progress and future challenges. Appl. Phys. Rev. 1 March 2022; 9 (1): 011405. https://doi.org/10.1063/5.0076324
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