Toward autonomous design and synthesis of novel inorganic materials
Abstract
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve “closed-loop” materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Lastly, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.
- Authors:
-
[1];
[1];
[1];
[2];
[1]
- Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Stony Brook Univ., NY (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; National Science Foundation Graduate Research Fellowship; USDOE
- OSTI Identifier:
- 1865712
- Alternate Identifier(s):
- OSTI ID: 1784793; OSTI ID: 1828572
- Grant/Contract Number:
- SC0019212; AC02-05-CH11231; 1752814; AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Materials Horizons
- Additional Journal Information:
- Journal Volume: 8; Journal Issue: 8; Journal ID: ISSN 2051-6347
- Publisher:
- Royal Society of Chemistry
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Szymanski, Nathan J., Zeng, Yan, Huo, Haoyan, Bartel, Christopher J., Kim, Haegyeom, and Ceder, Gerbrand. Toward autonomous design and synthesis of novel inorganic materials. United States: N. p., 2021.
Web. doi:10.1039/d1mh00495f.
Szymanski, Nathan J., Zeng, Yan, Huo, Haoyan, Bartel, Christopher J., Kim, Haegyeom, & Ceder, Gerbrand. Toward autonomous design and synthesis of novel inorganic materials. United States. https://doi.org/10.1039/d1mh00495f
Szymanski, Nathan J., Zeng, Yan, Huo, Haoyan, Bartel, Christopher J., Kim, Haegyeom, and Ceder, Gerbrand. Wed .
"Toward autonomous design and synthesis of novel inorganic materials". United States. https://doi.org/10.1039/d1mh00495f. https://www.osti.gov/servlets/purl/1865712.
@article{osti_1865712,
title = {Toward autonomous design and synthesis of novel inorganic materials},
author = {Szymanski, Nathan J. and Zeng, Yan and Huo, Haoyan and Bartel, Christopher J. and Kim, Haegyeom and Ceder, Gerbrand},
abstractNote = {Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve “closed-loop” materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Lastly, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.},
doi = {10.1039/d1mh00495f},
journal = {Materials Horizons},
number = 8,
volume = 8,
place = {United States},
year = {Wed May 19 00:00:00 EDT 2021},
month = {Wed May 19 00:00:00 EDT 2021}
}
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