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Piezoresponse Force Microscopy (PFM) has become one of the dominant techniques for exploring polar p

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Hypothesis-Driven Automated Experiment in Scanning Probe Microscopy: Exploring the Domain Growth Laws in Ferroelectric Materials 03/02/2022

Hypothesis-Driven Automated Experiment in Scanning Probe Microscopy: Exploring the Domain Growth Laws in Ferroelectric Materials

Can the machine learning algorithm run the microscope to learn physics of domain formation via exploring competing hypotheses?

Also, extension to other automated experiments is synthesis or scientific instrumentation is straightforward. Physics is all it takes.

Hypothesis-Driven Automated Experiment in Scanning Probe Microscopy: Exploring the Domain Growth Laws in Ferroelectric Materials We report the development and implementation of a hypothesis learning based automated experiment, in which the microscope operating in the autonomous mode identifies the physical laws behind the material's response. Specifically, we explore the bias induced transformations that underpin the function...

Switching Spectroscopy Piezoresponse Force Microscopy (SS-PFM) 16/11/2021

Switching Spectroscopy Piezoresponse Force Microscopy (SS-PFM)

As an interesting development, looks like Bruker has implemented the commercial version of the SS-PFM mode with the option for complex spectroscopies. This is really great, since now systematic studies of polarization dynamics are becoming possible on commercial tools!

Exactly 15 years after the Rev. Sci. Instr. paper introducing this mode for the first time, https://aip.scitation.org/doi/abs/10.1063/1.2214699, and adding more complex capabilities as in https://aip.scitation.org/doi/abs/10.1063/1.2980031 and https://pubs.acs.org/doi/abs/10.1021/nn505176a

https://www.bruker.com/en/products-and-solutions/microscopes/materials-afm/afm-modes/ss-pfm.html

Switching Spectroscopy Piezoresponse Force Microscopy (SS-PFM) Switching Spectroscopy Piezoresponse Force Microscopy (SS-PFM) mode provides highly accurate ferroelectric hysteresis loop measurement by improving the sensitivity and accuracy of PFM

Ferroelectric 2D ice under graphene confinement - Nature Communications 02/11/2021

Ferroelectric 2D ice under graphene confinement - Nature Communications

Very important paper for ferroelectric and PFM community - if we remember that there is water meniscus between tip and the surface.

Ferroelectric 2D ice under graphene confinement - Nature Communications Ferroelectric ordering of water has been at the heart of intense debates due to its importance in enhancing our understanding of the condensed matter. Here, the authors observe ferroelectric properties of water ice in a two dimensional phase under confinement between two graphene layers.

Machine Learning and Automated Experiments in Scanning Probe Microscopy Virtual School Recordings, October 4-7, 2021 | ORNL 29/10/2021

Machine Learning and Automated Experiments in Scanning Probe Microscopy Virtual School Recordings, October 4-7, 2021 | ORNL

Lectures from ORNL workshop on Automated Experiment and Machine Learning in Scanning Probe Microscopy -> online. Featuring lectures by Mikhail Katsnelson , Leroy Cronin, Danilo J. Rezende , and many others, as well as tutorials on variational autoencoders for image and spectral analysis, Bayesian Optimization, and deep convolutional networks and deep kernel learning. See:

Machine Learning and Automated Experiments in Scanning Probe Microscopy Virtual School Recordings, October 4-7, 2021 | ORNL Organizers: Rama Vasudevan, Kyle Kelley, Maxim Ziatdinov, Josh Agar, and Sergei V. Kalinin This virtual school on ML and AE in SPM, held October 4-7, 2021, combined invited and contributed presentations at the forefront of ML applications in Scanning Probe Microscopy, including both atomically resol...

Probing polarization dynamics at specific domain configurations: Computer-vision based automated experiment in piezoresponse force microscopy 01/10/2021

Probing polarization dynamics at specific domain configurations: Computer-vision based automated experiment in piezoresponse force microscopy

Some automated experiment in PFM - based on prior-known descriptors. Aka define what domain wall you want to study, find it, and measure polarization loops or current-voltage curves - right at the wall, or at some separation from it, as a function of curvature, or other characteristics.

Probing polarization dynamics at specific domain configurations: Computer-vision based automated experiment in piezoresponse force microscopy Topological defects in ferroelectric materials have attracted much attention due to the emergence of conductive, ferroic, and magnetic functionalities. However, many topological configurations dyna...

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications 31/08/2021

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

Can multi objective Bayesian Optimization help find right Ferro (or antiferro) electric for specific application? Especially with chemical (or interfacial) control?

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications Optimization of materials performance for specific applications often requires balancing multiple aspects of materials functionality. Even for the cases where generative physical model of material behavior is known and reliable, this often requires search over multidimensional parameter space to ide...

Machine Learning and Automated Experiment in Scanning Probe Microscopy 20/07/2021

Machine Learning and Automated Experiment in Scanning Probe Microscopy

We are excited to invite you to the virtual school “Machine Learning and Automated Experiment in Scanning Probe Microscopy” to be held at ORNL, October 4-7, 2021, organized by Rama Vasudevan, Kyle Kelley, Maxim Ziatdinov, Josh Agar, and Sergei V. Kalinin. Due to the close similarity between SPM and STEM, these topics may also be of interest for STEM community as well.

The registration deadline is August 25. Link to register:

https://www.surveymonkey.com/r/VDWFV5K

Machine learning (ML) has emerged as a powerful tool for data and image analysis and as an enabling component of autonomous systems in areas ranging from biological and medical imaging to self-driving cars. This rapid growth in ML applications poses the question as to which of these methods can be applied in scanning probe microscopy, both to derive insights into the physics and chemistry of real materials, enable optimization of imaging conditions, and engender transition to the automated and autonomous experiment. This virtual school on ML and AE for SPM, to be held virtually on October 4-7, 2021, will combine invited and contributed presentations at the forefront of ML applications in Scanning Probe Microscopy, including both atomically resolved Scanning Tunneling Microscopy and Spectroscopy and mesoscopic Scanning Probe Microscopy techniques.

Special emphasis will be made on necessary conditions for physically-meaningful machine learning analysis and especially automated experiments in SPM. It will feature tutorials on recent developments in ML analysis of mesoscopic and atomically resolved images and spectroscopy, including deep convolutional neural networks (DCNNs) for feature identifications, symmetry-invariant (variational) autoencoders ((V)AE), and Gaussian Processes and Deep Kernel Learning-based super-resolution imaging and image reconstruction, and reinforcement learning for image optimization and automated experiment. The presentations will be followed by hands-on tutorial sessions introducing the attendees to the AtomAI, GPim, PyroVED, and various Pycroscopy packages. All the technologies and workflows discussed during the tutorials will be open source. The attendees are encouraged to contact the organizers in advance to setup analysis of own datasets. The meeting will be free of charge. The final program will be available by September 2021.

If interested, please contact Sergei Kalinin ([email protected]) to submit the abstract.

Machine Learning and Automated Experiment in Scanning Probe Microscopy Take this survey powered by surveymonkey.com. Create your own surveys for free.

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics 07/07/2021

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

Using Bayesian Optimization to run Piezoresponse Force Microscopy experiment. Kudos to Rama Vasudevan for connecting DGX box to microscope, and Maxim Ziatdinov for writing the GPim library. Next stop - deep kernel learning!

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

Effect of surface ionic screening on polarization reversal and phase diagrams in thin antiferroelectric films for information and energy storage 25/06/2021

Effect of surface ionic screening on polarization reversal and phase diagrams in thin antiferroelectric films for information and energy storage

Effect of surface ionic screening on polarization reversal and phase diagrams in thin antiferroelectric films for information and energy storage The emergent behaviors in the antiferroelectric thin films due to coupling between surface electrochemistry and intrinsic polar, and structural instabilities are explored using the modified 2-4-6 Kittel-Landau-Ginzburg-Devonshire (KLGD) thermodynamic approach. The two polarization sublattices model....

Disentangling ferroelectric wall dynamics and identification of pinning mechanisms via deep learning 19/05/2021

Disentangling ferroelectric wall dynamics and identification of pinning mechanisms via deep learning

For quite a while, we have been pondering how to describe mechanisms underpinning domain wall dynamics in ferroelectric materials from real space observations. Now, combination of multilayer rotationally invariant variational autoencoders (mrVAE) by Maxim Ziatdinov, excellent PFM data by Roger Proksch on the Roger Proksch - polished PZT (RPP - PZT), and insight, persistence, and effort of Yongtao Liu in putting these together make it possible!

Disentangling ferroelectric wall dynamics and identification of pinning mechanisms via deep learning Field-induced domain wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled non-linearities, or....

12/04/2021

Dynamic manipulation in piezoresponse force microscopy: creating non-equilibrium phases with large electromechanical response

Dear colleagues

As a new development, for the last year the CNMS has been actively developing the synergy of the machine learning and direct image based-feedbacks to the operational SPM, i.e. automated experiment. As an example, we can collect the line or image signal, deploy the specific image analytics algorithm on top of it, and dependent perform specific action within the image plane based on these analyses. The set of these forms workflow for automated experiment.

As in many cases before, these capabilities have first been implemented for PFM. For example, we have demonstrated approaches to apply specific bias pulses exclusively at ferroelectric domain walls towards exploring their dynamics. Alternatively, we can identify the objects of interest in the image (e.g. regions of ferroelectric domain walls with high curvature) and perform piezoresponse or current-voltage spectroscopy only at these objects, etc. Some of the initial results are available as:

https://arxiv.org/abs/2001.03586

https://arxiv.org/abs/2004.11817

https://arxiv.org/abs/2011.13050

https://arxiv.org/abs/2103.12165

In this regard, we seek to open these opportunities for the user access, supported by the synergy of Data NanoAnalytics group (GL – Sergei Kalinin) and SPM group (GL – Stephen Jesse). Please let me know if you will be interested in exploring this opportunity, and we will be delighted to work with you to formulate it as a user proposal.

The relevant submission deadline will be May 5 (i.e. three weeks from now), leaving ample time for brainstorming and proposal development.

Dynamic manipulation in piezoresponse force microscopy: creating non-equilibrium phases with large electromechanical response Domains walls and topological defects in ferroelectric materials have emerged as a powerful new paradigm for functional electronic devices including memory and logic. Similarly, wall interactions and dynamics underpin a broad range of mesoscale phenomena ranging from giant electromechanical response...

How we learnt to love the rotationally invariant variational autoencoders (rVAE), and (almost)… 27/02/2021

How we learnt to love the rotationally invariant variational autoencoders (rVAE), and (almost)…

How we learnt to love the rotationally invariant variational autoencoders (rVAE), and (almost)… Maxim Ziatdinov¹ ² & Sergei V. Kalinin¹

Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables 09/01/2021

Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables

The encoder-decoder networks can be used to distill structure-property relationships in ferroelectrics from observational data via passing observed structure through latent bottleneck and expanding it to property descriptor. This further gives access to latent variables maps as measures of material behavior, and prediction uncertainties as a parameter to guide automated experiment.

Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables Polarization switching mechanisms in ferroelectric materials are fundamentally linked to local domain structure and the presence of the structural defects, which both can act as nucleation and pinning centers and create local electrostatic and mechanical depolarization fields affecting wall dynamics...

Postdoctoral Research Associate - Machine Learning-guided Scanning Tunneling Microscopy 08/09/2020

Postdoctoral Research Associate - Machine Learning-guided Scanning Tunneling Microscopy

Dear colleagues - we are looking for a postdoc candidate with UHV STM experience and knowledge/strong interest to Python ML. Please share and PM me.

Postdoctoral Research Associate - Machine Learning-guided Scanning Tunneling Microscopy Postdoctoral Research Associate - Machine Learning-guided Scanning Tunneling Microscopy

Machine Learning Mesoscopic Phenomena in Ferroelectrics | IEEE IFCS-ISAF 2020 19/07/2020

Machine Learning Mesoscopic Phenomena in Ferroelectrics | IEEE IFCS-ISAF 2020

For ferroelectric enthusiasts - a tutorial on what machine learning can do!

Machine Learning Mesoscopic Phenomena in Ferroelectrics | IEEE IFCS-ISAF 2020 Platinum Patrons Gold Patrons Web Patrons Conference Sponsors Technical Sponsor Welcome Schedule Tracks Search Affiliates Contact Follow IEEE IFCS-ISAF 2020 Follow IEEE IFCS-ISAF on Facebook and Linkedin! -ISAF2020 CONFlux Platform Powered By Home  |  Sitemap/More Sites  |  Contact & Suppor...

Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning 14/07/2020

Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning

Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning Domain switching pathways in ferroelectric materials visualized via dynamic Piezoresponse Force Microscopy are explored via rotationally invariant variational autoencoders (rVAE). rVAEs simplify the elements of the observed domain structure, crucially allowing for rotational invariance, thereby redu...

Postdoctoral Research Associate - Nanofabrication Lab 05/02/2020

Postdoctoral Research Associate - Nanofabrication Lab

Dear colleagues - if you or someone you know are interested in automated experiment in Scanning Probe Microscopy and specifically Piezoresponse Force Microscopy, now we have position open!

Postdoctoral Research Associate - Nanofabrication Lab Postdoctoral Research Associate - Nanofabrication Lab

Dynamic manipulation in piezoresponse force microscopy: creating non-equilibrium phases with large electromechanical response 13/01/2020

Dynamic manipulation in piezoresponse force microscopy: creating non-equilibrium phases with large electromechanical response

Self-driving microscope: For 30 years, scanning probe microscopes performed a predefined set of operations. Now we have incorporated automated experiment workflow in Piezoresponse Force Microscopy, when microscope performs operation depending on detected feature. As a first try - creation of strongly nonequilibrim phases with Gian electromechanical response.

Kudos to Kyle Kelly and the whole CNMS SPM team to make it work!

Dynamic manipulation in piezoresponse force microscopy: creating non-equilibrium phases with large electromechanical response Domains walls and topological defects in ferroelectric materials have emerged as a powerful new paradigm for functional electronic devices including memory and logic. Similarly, wall interactions and dynamics underpin a broad range of mesoscale phenomena ranging from giant electromechanical response...

03/12/2019

Dear Colleagues and Friends! Please come to the next PFM conference in Ekaterinburg in August 2020! Visit the conference web page https://nanocenter.urfu.ru/pfm2020spm. Note that next year it will be combined with the International Conference on Scanning Probe Microscopy and Russian Conference on Ferroelectricity! You are all welcome to Russia!

M*N: Microscopy, Machine Learning, Materials 27/09/2019

M*N: Microscopy, Machine Learning, Materials

Dear colleagues

The YouTube channel "M*N: Microscopy, Machine Learning, Materials" dedicated to the applications of big data, machine learning, and artificial intelligence in Scanning Transmission Electron Microscopy and Scanning Probe Microscopy is now fully updated with the historical overview lectures. These include:

1. Unsupervised learning in Scanning Probe Microscopy: Spectroscopies
2. Supervised learning in Scanning Probe Microscopy: Spectroscopies
3. Linear unmixing: basic techniques and some applications in microscopy and spectroscopy
4. Supervised and unsupervised learning in Scanning Probe Microscopy: Imaging
5. Supervised and Unsupervised Learning in Scanning Transmission Electron Microscopy
6. Learning Physics (and Chemistry) from Scanning Transmission Electron Microscopy
7. Atomic fabrication by STEM: feedback, compressed sensing, and non-rectangular beam paths

The channel now also features the "Z Corner" list started Maxim Ziatdinov, containing the short tutorial lectures on recent developments on ML in STEM, including:
1. Jupyter papers: scientific papers with data and code
2. Introduction to deep learning with PyTorch in Google Colab
3. How to work with AICrystallographer in Google Colab
The new lectures are becoming available once the associated Jupyter notebooks are developed.

The channel is available at:

https://www.youtube.com/channel/UCyh-7XlL-BuymJD7vdoNOvw

These lectures are closely tied to the on-line data and code resources. The codes are (or will be) shared via the PyCroscopy domain on the GitHub.
https://github.com/pycroscopy/pyCroscopy

Subscribe, join, and stay tuned!

M*N: Microscopy, Machine Learning, Materials Lectures on Scanning Probe Microscopy: Piezoresponse Force Microscopy, Electrochemical Strain Microscopy, and Kelvin Probe Force Microscopy

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