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Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams

Ament, Sebastian and Amsler, Maximilian and Sutherland, Duncan R. and Chang, Ming-Chiang and Guevarra, Dan and Connolly, Aine B. and Gregoire, John M. and Thompson, Michael O. and Gomes, Carla P. and van Dover, R. Bruce (2021) Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams. Science Advances, 7 (51). Art. No. abg4930. ISSN 2375-2548. PMCID PMC8682983. doi:10.1126/sciadv.abg4930.

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Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA’s performance by autonomously mapping synthesis phase boundaries for the Bi₂O₃ system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for stabilizing δ-Bi₂O₃ at room temperature, a critical development for electrochemical technologies.

Item Type:Article
Related URLs:
URLURL TypeDescription Information CentralArticle
Ament, Sebastian0000-0001-6316-4633
Amsler, Maximilian0000-0001-8350-2476
Sutherland, Duncan R.0000-0002-8171-5053
Chang, Ming-Chiang0000-0003-1880-6654
Guevarra, Dan0000-0002-9592-3195
Connolly, Aine B.0000-0002-2721-5621
Gregoire, John M.0000-0002-2863-5265
Thompson, Michael O.0000-0002-4933-009X
Gomes, Carla P.0000-0002-4441-7225
van Dover, R. Bruce0000-0002-6166-5650
Additional Information:© 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). Received: 19 January 2021. Accepted: 29 October 2021. Published 17 December 2021. We acknowledge the Air Force Office of Scientific Research for support under award FA9550-18-1-0136. This work is based on research conducted at the Materials Solutions Network at CHESS (MSN-C), which is supported by the Air Force Research Laboratory under award FA8650-19-2-5220, and the NSF Expeditions under award CCF-1522054. This work was also performed, in part, at the Cornell NanoScale Facility, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the NSF (grant NNCI-2025233). M.A. acknowledges support from the Swiss National Science Foundation (project P4P4P2-180669). This research was conducted with support from the Cornell University Center for Advanced Computing. Author contributions: R.B.v.D., C.P.G., M.O.T., and J.M.G. conceived and supervised the research. S.A. and M.A. developed and implemented the SARA algorithms and contributed equally to this work. M.A. and S.A. took the lead in writing the manuscript. D.R.S. fabricated the Bi2O3 thin-film samples and collected and analyzed the optical microscopy and reflectance data. A.B.C. performed the lg-LSA experiments. D.G. and J.M.G. processed the XRD data, and D.R.S. and M.-C.C. helped analyze the results. All authors provided critical feedback and helped shape the research, analysis, and manuscript. The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9550-18-1-0136
Air Force Office of Scientific Research (AFOSR)FA8650-19-2-5220
Swiss National Science Foundation (SNSF)P4P4P2-180669
Cornell UniversityUNSPECIFIED
Issue or Number:51
PubMed Central ID:PMC8682983
Record Number:CaltechAUTHORS:20211220-590745000
Persistent URL:
Official Citation:Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams. Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Ming-Chiang Chang, Dan Guevarra, Aine B. Connolly, John M. Gregoire, Michael O. Thompson, Carla P. Gomes, R. Bruce van Dover. Sci. Adv., 7 (51), eabg4930; DOI: 10.1126/sciadv.abg4930
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112555
Deposited By: George Porter
Deposited On:20 Dec 2021 18:48
Last Modified:25 Jul 2022 23:15

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