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

Cited by 29 publications

(18 citation statements)

References 40 publications

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Section: Resultsmentioning

confidence: 99%

“…These approaches are illustrated in Figure Figure a–c illustrates the GP reconstruction of the image based on a fixed sparse scanning pattern.…”

Section: Resultsmentioning

confidence: 99%

“…This has generated much enthusiasm on combining machine-learning-based data analysis within the microscope data acquisition loop, i.e., the concept of automated and autonomous microscopy. Correspondingly, a number of opinion and analysis pieces have been published over the past few years. ,,,− …”

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confidence: 99%

“…Recently, we have critically explored the potential of ML methods for automated microscopies . Here, we illustrate these approaches for SPM (Figure ) and demonstrate the development of ML-based experimental workflows based on Bayesian methods.…”

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confidence: 99%

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Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

et al. 2022

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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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Section: Resultsmentioning

confidence: 99%

“…These approaches are illustrated in Figure Figure a–c illustrates the GP reconstruction of the image based on a fixed sparse scanning pattern.…”

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

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“…Generally, this involves building specific architectures for supervised learning (classification or regression), unsupervised latent space extraction, generative models, control systems, and much more. Deep learning has recently been used to extract latent manifolds from high-dimensional spectroscopy, discover phase transformations, segmentation, and detection in microscopy images, and controlled experimentation and atomic manipulation …”

Section: M3i3 Challenges and Future Perspectivementioning

confidence: 99%

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

et al. 2021

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Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing–structure–property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure–property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni–Co–Mn cathode materials illustrates M3I3’s approach to creating libraries of multiscale structure–property–processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

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Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

2022

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Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics-based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk-and edge plasmon discovery in MnPS 3 , a lesser-known beam-sensitive layered 2D material. This approach is universal, can be directly used as-is with any specimen, and is expected to be applicable to any probe-based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging.

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

Cited by 29 publications

References 40 publications

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

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