Abstract
Using hypothesis-learning-driven automated scanning probe microscopy (SPM), we explore the bias-induced transformations that underpin the functionality of broad classes of devices and materials from batteries and memristors to ferroelectrics and antiferroelectrics. Optimization and design of these materials require probing the mechanisms of these transformations on the nanometer scale as a function of a broad range of control parameters, leading to experimentally intractable scenarios. Meanwhile, often these behaviors are understood within potentially competing theoretical hypotheses. Here, we develop a hypothesis list covering possible limiting scenarios for domain growth in ferroelectric materials, including thermodynamic, domain-wall pinning, and screening limited. The hypothesis-driven SPM autonomously identifies the mechanisms of bias-induced domain switching, and the results indicate that domain growth is ruled by kinetic control. We note that the hypothesis learning can be broadly used in other automated experiment settings.
| Original language | English |
|---|---|
| Article number | 100704 |
| Journal | Patterns |
| Volume | 4 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 10 2023 |
Funding
The authors acknowledge Stephen Jesse for helpful discussions and implementation of hardware-software integration and Vladimir A. Protopopescu for helpful discussions. This effort (physical hypotheses development, AE implementation, measurements) was supported as part of the center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the US Department of Energy (DOE), Office of Science , Basic Energy Sciences under award number DE-SC0021118 . The research (hypothesis-learning development) was performed and partially supported at Oak Ridge National Laboratory ’s Center for Nanophase Materials Sciences (CNMS), a US DOE, Office of Science User Facility . A.N.M. was supported by the National Academy of Sciences of Ukraine and received funding from the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 778070 . This manuscript has been authored by UT-Battelle, LLC, under contract no. DE-AC0500OR22725 with the US DOE. The United States government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). The authors acknowledge Stephen Jesse for helpful discussions and implementation of hardware-software integration and Vladimir A. Protopopescu for helpful discussions. This effort (physical hypotheses development, AE implementation, measurements) was supported as part of the center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences under award number DE-SC0021118. The research (hypothesis-learning development) was performed and partially supported at Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a US DOE, Office of Science User Facility. A.N.M. was supported by the National Academy of Sciences of Ukraine and received funding from the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 778070. This manuscript has been authored by UT-Battelle, LLC, under contract no. DE-AC0500OR22725 with the US DOE. The United States government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Y.L. implemented the hypothesis-learning microscopy. Y.L. developed the hypothesis-learning microscope automation method with help from K.P.K. and R.V. Y.L. collected and analyzed the data. M.Z. realized the hypothesis-learning algorithms and provided the initial script. S.V.K. proposed and led the research. A.N.M. and E.A.E. developed the analytical models. All authors participated in the discussion. The authors have submitted a patent about hypothesis-learning-driven automated experiment. We support inclusive, diverse, and equitable conduct of research.
Keywords
- DSML2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
- automated experiment
- ferroelectrics
- hypothesis learning
- scanning probe microscopy