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Raghavan A, Pant R, Takeuchi I, Eliseev EA, Checa M, Morozovska AN, Ziatdinov M, Kalinin SV, Liu Y. Evolution of Ferroelectric Properties in Sm xBi 1-xFeO 3 via Automated Piezoresponse Force Microscopy across combinatorial spread libraries. ACS NANO 2024; 18:25591-25600. [PMID: 39241038 DOI: 10.1021/acsnano.4c06380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/08/2024]
Abstract
Combinatorial spread libraries offer an approach to explore the evolution of material properties over broad concentration, temperature, and growth parameter spaces. However, the traditional limitation of this approach is the requirement for the read-out of functional properties across the library. Here we develop automated piezoresponse force microscopy (PFM) for the exploration of combinatorial spread libraries and demonstrate its application in the SmxBi1-xFeO3 system with the ferroelectric-antiferroelectric morphotropic phase boundary. This approach relies on the synergy of the quantitative nature of PFM and the implementation of automated experiments that allow PFM-based sampling of macroscopic samples. The concentration dependence of pertinent ferroelectric parameters was determined and used to develop the mathematical framework based on the Ginzburg-Landau theory describing the evolution of these properties across the concentration space. We pose that a combination of automated scanning probe microscope and combinatorial spread library approach will emerge as an efficient research paradigm to close the characterization gap in high-throughput materials discovery. We make the data sets open to the community, and we hope that this will stimulate other efforts to interpret and understand the physics of these systems.
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Affiliation(s)
- Aditya Raghavan
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37909, United States
| | - Rohit Pant
- Deparment of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Ichiro Takeuchi
- Deparment of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Eugene A Eliseev
- Frantsevich Institute for Problems in Materials Science, Omeliana Pritsaka str., 3, 03142 Kyiv, Ukraine
| | - Marti Checa
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Anna N Morozovska
- Institute of Physics, National Academy of Sciences of Ukraine, 46, pr. Nauky, 03028 Kyiv, Ukraine
| | - Maxim Ziatdinov
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37909, United States
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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2
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Liu Y, Ziatdinov MA, Vasudevan RK, Kalinin SV. Explainability and human intervention in autonomous scanning probe microscopy. PATTERNS (NEW YORK, N.Y.) 2023; 4:100858. [PMID: 38035198 PMCID: PMC10682748 DOI: 10.1016/j.patter.2023.100858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 07/26/2023] [Accepted: 09/15/2023] [Indexed: 12/02/2023]
Abstract
The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Maxim A. Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Rama K. Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Sergei V. Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA
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3
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Liu Y, Kelley KP, Vasudevan RK, Ziatdinov M, Kalinin SV. Machine Learning-driven Autonomous Microscopy for Materials and Physics Discovery. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1866-1867. [PMID: 37613961 DOI: 10.1093/micmic/ozad067.963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, The University of Tennessee, Knoxville, TN, USA
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4
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Liu Y, Yang J, Lawrie BJ, Kelley KP, Ziatdinov M, Kalinin SV, Ahmadi M. Disentangling Electronic Transport and Hysteresis at Individual Grain Boundaries in Hybrid Perovskites via Automated Scanning Probe Microscopy. ACS NANO 2023; 17:9647-9657. [PMID: 37155579 DOI: 10.1021/acsnano.3c03363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advancement in the understanding of the microstructure of polycrystalline MHP thin film. Over the past decade, intense efforts have been aimed at understanding the effect of microstructures on MHP properties, including chemical heterogeneity, strain disorder, phase impurity, etc. It has been found that grain and grain boundary (GB) are tightly related to lots of microscale and nanoscale behavior in MHP thin films. Atomic force microscopy (AFM) is widely used to observe grain and boundary structures in topography and subsequently to study the correlative surface potential and conductivity of these structures. For now, most AFM measurements have been performed in imaging mode to study the static behavior; in contrast, AFM spectroscopy mode allows us to investigate the dynamic behavior of materials, e.g., conductivity under sweeping voltage. However, a major limitation of AFM spectroscopy measurements is that they require manual operation by human operators, and as such only limited data can be obtained, hindering systematic investigations of these microstructures. In this work, we designed a workflow combining the conductive AFM measurement with a machine learning (ML) algorithm to systematically investigate grain boundaries in MHPs. The trained ML model can extract GBs locations from the topography image, and the workflow drives the AFM probe to each GB location to perform a current-voltage (IV) curve automatically. Then, we are able to have IV curves at all GB locations, allowing us to systematically understand the property of GBs. Using this method, we discovered that the GB junction points are less conductive, potentially more photoactive, and can play critical roles in MHP stability, while most previous works only focused on the difference between GB and grains.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Jonghee Yang
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Benjamin J Lawrie
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Mahshid Ahmadi
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
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Liu Y, Yang J, Vasudevan RK, Kelley KP, Ziatdinov M, Kalinin SV, Ahmadi M. Exploring the Relationship of Microstructure and Conductivity in Metal Halide Perovskites via Active Learning-Driven Automated Scanning Probe Microscopy. J Phys Chem Lett 2023; 14:3352-3359. [PMID: 36994975 DOI: 10.1021/acs.jpclett.3c00223] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These phenomena are strongly affected by the materials microstructure including grain boundaries, ferroic domain walls, and secondary phase inclusions. Here, we demonstrate an active machine learning framework for "driving" an automated scanning probe microscope (SPM) to discover the microstructures responsible for specific aspects of transport behavior in MHPs. In our setup, the microscope can discover the microstructural elements that maximize the onset of conduction, hysteresis, or any other characteristic that can be derived from a set of current-voltage spectra. This approach opens new opportunities for exploring the origins of materials functionality in complex materials by SPM and can be integrated with other characterization techniques either before (prior knowledge) or after (identification of locations of interest for detail studies) functional probing.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Jonghee Yang
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Mahshid Ahmadi
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
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Yaman MY, Kalinin SV, Guye KN, Ginger DS, Ziatdinov M. Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023:e2205893. [PMID: 36942857 DOI: 10.1002/smll.202205893] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/27/2023] [Indexed: 06/18/2023]
Abstract
The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.
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Affiliation(s)
- Muammer Y Yaman
- Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, 37996, USA
| | - Kathryn N Guye
- Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
| | - David S Ginger
- Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
- Physical Sciences Division, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
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Liu Y, Kelley KP, Vasudevan RK, Zhu W, Hayden J, Maria JP, Funakubo H, Ziatdinov MA, Trolier-McKinstry S, Kalinin SV. Automated Experiments of Local Non-Linear Behavior in Ferroelectric Materials. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2204130. [PMID: 36253123 DOI: 10.1002/smll.202204130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/19/2022] [Indexed: 06/16/2023]
Abstract
An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function is developed and implemented. Here, the emergence of non-linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non-linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip-surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non-linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al0.93 B0.07 N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al0.93 B0.07 N, well-poled regions show high linear piezoelectric responses, when paired with low non-linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non-linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. This technique can also be extended to electron, probe, and chemical imaging.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Wanlin Zhu
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - John Hayden
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Jon-Paul Maria
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Dielectrics and Piezoelectrics, Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Hiroshi Funakubo
- Department of Material Science and Engineering, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Maxim A Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Susan Trolier-McKinstry
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Dielectrics and Piezoelectrics, Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, 37916, USA
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Liu Y, Kelley KP, Funakubo H, Kalinin SV, Ziatdinov M. Exploring Physics of Ferroelectric Domain Walls in Real Time: Deep Learning Enabled Scanning Probe Microscopy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203957. [PMID: 36065001 PMCID: PMC9631058 DOI: 10.1002/advs.202203957] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/12/2022] [Indexed: 05/25/2023]
Abstract
The functionality of ferroelastic domain walls in ferroelectric materials is explored in real-time via the in situ implementation of computer vision algorithms in scanning probe microscopy (SPM) experiment. The robust deep convolutional neural network (DCNN) is implemented based on a deep residual learning framework (Res) and holistically nested edge detection (Hed), and ensembled to minimize the out-of-distribution drift effects. The DCNN is implemented for real-time operations on SPM, converting the data stream into the semantically segmented image of domain walls and the corresponding uncertainty. Further the pre-defined experimental workflows perform piezoresponse spectroscopy measurement on thus discovered domain walls, and alternating high- and low-polarization dynamic (out-of-plane) ferroelastic domain walls in a PbTiO3 (PTO) thin film and high polarization dynamic (out-of-plane) at short ferroelastic walls (compared with long ferroelastic walls) in a lead zirconate titanate (PZT) thin film is reported. This work establishes the framework for real-time DCNN analysis of data streams in scanning probe and other microscopies and highlights the role of out-of-distribution effects and strategies to ameliorate them in real time analytics.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials SciencesOak Ridge National LaboratoryOak RidgeTN37830USA
| | - Kyle P. Kelley
- Center for Nanophase Materials SciencesOak Ridge National LaboratoryOak RidgeTN37830USA
| | - Hiroshi Funakubo
- Department of Material Science and EngineeringTokyo Institute of TechnologyYokohama226‐8502Japan
| | - Sergei V. Kalinin
- Department of Materials Science and EngineeringUniversity of TennesseeKnoxvilleTN37996USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials SciencesOak Ridge National LaboratoryOak RidgeTN37830USA
- Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeTN37830USA
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Qin S, Guo Y, Kaliyev AT, Agar JC. Why it is Unfortunate that Linear Machine Learning "Works" so well in Electromechanical Switching of Ferroelectric Thin Films. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202814. [PMID: 35906007 DOI: 10.1002/adma.202202814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band-excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so-called "better", "faster", and "less-biased" ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long-short-term memory (LSTM) β-variational autoencoders (β-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. To improve the interpretability of the latent space, a variational Kullback-Leibler-divergency regularization is imposed . Finally, regularization scheduling of β as a disentanglement metric is leveraged to reduce user bias. Combining these experiment-inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two-step, three-state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies.
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Affiliation(s)
- Shuyu Qin
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA
| | - Yichen Guo
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA
| | - Alibek T Kaliyev
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA
- College of Business, Lehigh University, Bethlehem, PA, 18015, USA
| | - Joshua C Agar
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA
- Department of Mechanical Engineering and Mechanics, Drexel University, Philadelphia, PA, 19104, USA
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10
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Ziatdinov M, Liu Y, Kelley K, Vasudevan R, Kalinin SV. Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning. ACS NANO 2022; 16:13492-13512. [PMID: 36066996 DOI: 10.1021/acsnano.2c05303] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. The discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.
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Affiliation(s)
| | | | | | | | - Sergei V Kalinin
- Department of Materials Sciences and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
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Thong H, Li Z, Lu J, Li C, Liu Y, Sun Q, Fu Z, Wei Y, Wang K. Domain Engineering in Bulk Ferroelectric Ceramics via Mesoscopic Chemical Inhomogeneity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2200998. [PMID: 35434943 PMCID: PMC9189658 DOI: 10.1002/advs.202200998] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/09/2022] [Indexed: 05/27/2023]
Abstract
Domain engineering in ferroelectrics endows flexibility for different functional applications. Whereas the domain engineering strategy for single crystals and thin films is diverse, there is only a limited number of strategies for bulk ceramics. Here, a domain engineering strategy for achieving a compact domain architecture with increased domain-wall density in (K,Na)NbO3 (KNN)-based ferroelectric ceramics via mesoscopic chemical inhomogeneity (MCI) is developed. The MCI-induced interfaces can effectively hinder domain continuity and modify the domain configuration. Besides, the MCI effect also results in diffused phase transitions, which is beneficial for achieving enhanced thermal stability. Modulation of chemical inhomogeneity demonstrates great potential for engineering desirable domain configuration and properties in ferroelectric ceramics. Additionally, the MCI can be easily controlled by regulating the processing condition during solid-state synthesis, which is advantageous to industrial production.
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Affiliation(s)
- Hao‐Cheng Thong
- State Key Laboratory of New Ceramics and Fine ProcessingSchool of Materials Science and EngineeringTsinghua UniversityBeijing100084P. R. China
| | - Zhao Li
- State Key Laboratory of New Ceramics and Fine ProcessingSchool of Materials Science and EngineeringTsinghua UniversityBeijing100084P. R. China
| | - Jing‐Tong Lu
- State Key Laboratory of New Ceramics and Fine ProcessingSchool of Materials Science and EngineeringTsinghua UniversityBeijing100084P. R. China
| | - Chen‐Bo‐Wen Li
- State Key Laboratory of New Ceramics and Fine ProcessingSchool of Materials Science and EngineeringTsinghua UniversityBeijing100084P. R. China
| | - Yi‐Xuan Liu
- State Key Laboratory of New Ceramics and Fine ProcessingSchool of Materials Science and EngineeringTsinghua UniversityBeijing100084P. R. China
| | - Qiannan Sun
- Beijing Laboratory of Biomedical MaterialsDepartment of Geriatric DentistryPeking University School and Hospital of StomatologyBeijing100081P. R. China
| | - Zhengqian Fu
- State Key Laboratory of High Performance Ceramics and Superfine MicrostructuresShanghai Institute of CeramicsChinese Academy of SciencesShanghai200050P. R. China
| | - Yan Wei
- Beijing Laboratory of Biomedical MaterialsDepartment of Geriatric DentistryPeking University School and Hospital of StomatologyBeijing100081P. R. China
| | - Ke Wang
- State Key Laboratory of New Ceramics and Fine ProcessingSchool of Materials Science and EngineeringTsinghua UniversityBeijing100084P. R. China
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12
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Experimental discovery of structure–property relationships in ferroelectric materials via active learning. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00460-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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