Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Vasudevan, Rama K.; Kelley, Kyle P.; Hinkle, Jacob; Funakubo, Hiroshi; Jesse, Stephen; Kalinin, Sergei V.; Ziatdinov, Maxim

doi:10.1021/acsnano.0c10239

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Journal Article · Tue Jul 06 00:00:00 EDT 2021 · ACS Nano

DOI:https://doi.org/10.1021/acsnano.0c10239· OSTI ID:1817554

^[1]; ^[1]; ^[1]; Funakubo, Hiroshi ^[2]; ^[1]; ^[1]; ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Tokyo Inst. of Technology, Yokohama (Japan)

Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO₃, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. Furthermore, this approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1817554

Journal Information:: ACS Nano, Journal Name: ACS Nano Journal Issue: 7 Vol. 15; ISSN 1936-0851

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

References (39)

Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation Kalinin, Sergei V.; Valleti, Mani; Vasudevan, Rama K. Journal of Applied Physics, Vol. 128, Issue 16 https://doi.org/10.1063/5.0021762	journal	October 2020
Scientific AI in materials science: a path to a sustainable and scalable paradigm DeCost, Bl; Hattrick-Simpers, Jr; Trautt, Z. Machine Learning: Science and Technology, Vol. 1, Issue 3 https://doi.org/10.1088/2632-2153/ab9a20	journal	July 2020
Direct Imaging of the Relaxation of Individual Ferroelectric Interfaces in a Tensile-Strained Film Li, Linglong; Cao, Ye; Somnath, Suhas Advanced Electronic Materials, Vol. 3, Issue 4 https://doi.org/10.1002/aelm.201600508	journal	March 2017
Atomic-Level Sculpting of Crystalline Oxides: Toward Bulk Nanofabrication with Single Atomic Plane Precision Jesse, Stephen; He, Qian; Lupini, Andrew R. Small, Vol. 11, Issue 44 https://doi.org/10.1002/smll.201502048	journal	October 2015
Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization Kelley, Kyle P.; Ziatdinov, Maxim; Collins, Liam Small, Vol. 16, Issue 37 https://doi.org/10.1002/smll.202002878	journal	August 2020
Predictability of Localized Plasmonic Responses in Nanoparticle Assemblies Roccapriore, Kevin M.; Ziatdinov, Maxim; Cho, Shin Hum Small, Vol. 17, Issue 21 https://doi.org/10.1002/smll.202100181	journal	April 2021
Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables Kalinin, Sergei V.; Kelley, Kyle; Vasudevan, Rama K. ACS Applied Materials & Interfaces, Vol. 13, Issue 1 https://doi.org/10.1021/acsami.0c15085	journal	January 2021
Designing Algorithms To Aid Discovery by Chemical Robots Henson, Alon B.; Gromski, Piotr S.; Cronin, Leroy ACS Central Science, Vol. 4, Issue 7 https://doi.org/10.1021/acscentsci.8b00176	journal	July 2018
Rapid Construction of Fe–Co–Ni Composition-Phase Map by Combinatorial Materials Chip Approach Xing, Hui; Zhao, Bingbing; Wang, Yujie ACS Combinatorial Science, Vol. 20, Issue 3 https://doi.org/10.1021/acscombsci.7b00171	journal	January 2018
Dynamic Manipulation in Piezoresponse Force Microscopy: Creating Nonequilibrium Phases with Large Electromechanical Response Kelley, Kyle P.; Ren, Yao; Morozovska, Anna N. ACS Nano, Vol. 14, Issue 8 https://doi.org/10.1021/acsnano.0c04601	journal	August 2020
Fire up the atom forge Kalinin, Sergei V.; Borisevich, Albina; Jesse, Stephen Nature, Vol. 539, Issue 7630 https://doi.org/10.1038/539485a	journal	November 2016
How the doors to the nanoworld were opened Gerber, Christoph; Lang, Hans Peter Nature Nanotechnology, Vol. 1, Issue 1 https://doi.org/10.1038/nnano.2006.70	journal	October 2006
Autonomy in materials research: a case study in carbon nanotube growth Nikolaev, Pavel; Hooper, Daylond; Webber, Frederick npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.31	journal	October 2016
Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling Ziatdinov, Maxim; Kim, Dohyung; Neumayer, Sabine npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-0289-6	journal	March 2020
A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering Noack, Marcus M.; Yager, Kevin G.; Fukuto, Masafumi Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-48114-3	journal	August 2019
Advances in Kriging-Based Autonomous X-Ray Scattering Experiments Noack, Marcus M.; Doerk, Gregory S.; Li, Ruipeng Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-57887-x	journal	January 2020
Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels Noack, Marcus M.; Doerk, Gregory S.; Li, Ruipeng Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-74394-1	journal	October 2020
Artificial-intelligence-driven scanning probe microscopy Krull, A.; Hirsch, P.; Rother, C. Communications Physics, Vol. 3, Issue 1 https://doi.org/10.1038/s42005-020-0317-3	journal	March 2020
On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets Kusne, Aaron Gilad; Gao, Tieren; Mehta, Apurva Scientific Reports, Vol. 4, Issue 1 https://doi.org/10.1038/srep06367	journal	September 2014
Rapid multidimensional data acquisition in scanning probe microscopy applied to local polarization dynamics and voltage dependent contact mechanics Jesse, Stephen; Maksymovych, Peter; Kalinin, Sergei V. Applied Physics Letters, Vol. 93, Issue 11 https://doi.org/10.1063/1.2980031	journal	September 2008
An automated spin-assisted approach for molecular layer-by-layer assembly of crosslinked polymer thin films Chan, Edwin P.; Lee, Jung-Hyun; Chung, Jun Young Review of Scientific Instruments, Vol. 83, Issue 11 https://doi.org/10.1063/1.4767289	journal	November 2012
Suppressed polar distortion with enhanced Curie temperature in in-plane 90°-domain structure of a -axis oriented PbTiO ₃ Film Morioka, Hitoshi; Yamada, Tomoaki; Tagantsev, Alexander K. Applied Physics Letters, Vol. 106, Issue 4 https://doi.org/10.1063/1.4906861	journal	January 2015
Guided search for desired functional responses via Bayesian optimization of generative model: Hysteresis loop shape engineering in ferroelectrics Kalinin, Sergei V.; Ziatdinov, Maxim; Vasudevan, Rama K. Journal of Applied Physics, Vol. 128, Issue 2 https://doi.org/10.1063/5.0011917	journal	July 2020
Band excitation in scanning probe microscopy: sines of change Jesse, Stephen; Kalinin, Sergei V. Journal of Physics D: Applied Physics, Vol. 44, Issue 46 https://doi.org/10.1088/0022-3727/44/46/464006	journal	November 2011
Adaptive probe trajectory scanning probe microscopy for multiresolution measurements of interface geometry Ovchinnikov, Oleg S.; Jesse, S.; Kalinin, S. V. Nanotechnology, Vol. 20, Issue 25 https://doi.org/10.1088/0957-4484/20/25/255701	journal	June 2009
A rapid and automated relocation method of an AFM probe for high-resolution imaging Zhou, Peilin; Yu, Haibo; Shi, Jialin Nanotechnology, Vol. 27, Issue 39 https://doi.org/10.1088/0957-4484/27/39/395705	journal	August 2016
Direct atomic fabrication and dopant positioning in Si using electron beams with active real-time image-based feedback Jesse, Stephen; Hudak, Bethany M.; Zarkadoula, Eva Nanotechnology, Vol. 29, Issue 25 https://doi.org/10.1088/1361-6528/aabb79	journal	April 2018
Dynamic X-ray diffraction sampling for protein crystal positioning Scarborough, Nicole M.; Godaliyadda, G. M. Dilshan P.; Ye, Dong Hye Journal of Synchrotron Radiation, Vol. 24, Issue 1 https://doi.org/10.1107/S160057751601612X	journal	January 2017
Taking the Human Out of the Loop: A Review of Bayesian Optimization Shahriari, Bobak; Swersky, Kevin; Wang, Ziyu Proceedings of the IEEE, Vol. 104, Issue 1 https://doi.org/10.1109/JPROC.2015.2494218	journal	January 2016
A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise Kushner, H. J. Journal of Basic Engineering, Vol. 86, Issue 1 https://doi.org/10.1115/1.3653121	journal	March 1964
Materials in nanotechnology: New structures, new properties, new complexity Bonnell, Dawn A. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, Vol. 21, Issue 5 https://doi.org/10.1116/1.1600445	journal	September 2003
Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments Ren, Fang; Ward, Logan; Williams, Travis Science Advances, Vol. 4, Issue 4 https://doi.org/10.1126/sciadv.aaq1566	journal	April 2018
Organic synthesis in a modular robotic system driven by a chemical programming language Steiner, Sebastian; Wolf, Jakob; Glatzel, Stefan Science, Vol. 363, Issue 6423 https://doi.org/10.1126/science.aav2211	journal	November 2018
Dynamic scan control in STEM: spiral scans Sang, Xiahan; Lupini, Andrew R.; Unocic, Raymond R. Advanced Structural and Chemical Imaging, Vol. 2, Issue 1 https://doi.org/10.1186/s40679-016-0020-3	journal	June 2016
A self-driving microscope and the Atomic Forge Dyck, Ondrej; Jesse, Stephen; Kalinin, Sergei V. MRS Bulletin, Vol. 44, Issue 09 https://doi.org/10.1557/mrs.2019.211	journal	September 2019
Progress toward autonomous experimental systems for alloy development Boyce, Brad L.; Uchic, Michael D. MRS Bulletin, Vol. 44, Issue 4 https://doi.org/10.1557/mrs.2019.75	journal	April 2019
Accelerated Discovery of Metallic Glasses through Iteration of Machine Learning and High-Throughput Experiments Fang, Ren; Ward, Logan; Williams, Travis Materials Data Facility https://doi.org/10.18126/m2b06m	dataset	January 2018
Predictability of localized plasmonic responses in nanoparticle assemblies Roccapriore, Kevin M.; Ziatdinov, Maxim; Cho, Shin-Hum Zenodo https://doi.org/10.5281/zenodo.4663068	dataset	January 2021
Gaussian Processes for Machine Learning Rasmussen, Carl Edward; Williams, Christopher K. I. The MIT Press https://doi.org/10.7551/mitpress/3206.001.0001	book	January 2005

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Bayesian optimization
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automated experiments
electromechanics
ferroelectrics
optimization
piezoresponse force microscopy
polarization
probes
scanning probe microscopy
spectroscopy

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

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