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

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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 identif**ations, 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 identif**ation of pinning mechanisms via deep learning 19/05/2021

Disentangling ferroelectric wall dynamics and identif**ation 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 identif**ation 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...

jobs.ornl.gov 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.

jobs.ornl.gov Postdoctoral Research Associate - Machine Learning-guided Scanning Tunneling Microscopy

2020.ifcs-isaf-virtual.org 19/07/2020

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

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

2020.ifcs-isaf-virtual.org 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! #IFCS-ISAF2020 CONFlux Platform Powered By Home  |  Sitemap/More Sites  |  Contact & Suppor...

arxiv.org 14/07/2020

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

arxiv.org 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...

jobs.ornl.gov 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 specif**ally Piezoresponse Force Microscopy, now we have position open!

jobs.ornl.gov Postdoctoral Research Associate - Nanofabrication Lab

arxiv.org 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!

arxiv.org 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...

youtube.com 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!

youtube.com Lectures on Scanning Probe Microscopy: Piezoresponse Force Microscopy, Electrochemical Strain Microscopy, and Kelvin Probe Force Microscopy

youtube.com 27/09/2019

M*N: Microscopy, Machine Learning, Materials

youtube.com Lectures on Scanning Probe Microscopy: Piezoresponse Force Microscopy, Electrochemical Strain Microscopy, and Kelvin Probe Force Microscopy

colab.research.google.com 20/09/2019

Google Colaboratory

If you always wanted to do machine learning for PFM data and need to know how - ask Josh Agar. And use his Jupyter paper on own data!

colab.research.google.com

27/07/2019

Jupyter paper (paper with executable code)

A brief introduction to "Jupyter paper" concepts

youtube.com 30/05/2019

M*N: Microscopy, Machine Learning, Materials

We would like to bring to your attention the YouTube channel "M*N: Microscopy, Machine Learning, Materials" dedicated to the applications of big data methods, machine learning, and artificial intelligence in Scanning Probe Microscopy and Scanning Transmission Electron Microscopy. The channel is available at:
https://www.youtube.com/channel/UCyh-7XlL-BuymJD7vdoNOvw

We aim to create the vibrant environment for sharing recent advances in this rapidly growing field, as well as to cover some of the historical developments over last decade and notable achievements in (potentially) related fields. The channel will start with 7-lecture overview of some of the developments over the last decade, including:

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 lectures will be posted weekly (or close to it), with the first one being posted on-line today.

After this initial round of lectures, we plan to post lecture tutorials on recent developments in the field of big data, machine learning, and artificial intelligence in electron, scanning probe microscopy and chemical imaging, with the lectures being planned on:

- Deep learning in Scanning Transmission Electron Microscopy

- Deep Learning in Scanning Tunneling Microscopy

- Workflows for creation of STEM image libraries and their applications

- and many more



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

The libraries of STEM images are disseminated via the CITRINation platform.



Finally, the imaging tools can accessed via the Center for Nanophase Materials Sciences.

https://www.ornl.gov/facility/cnms

Subscribe, join, and stay tuned!

youtube.com Lectures on Scanning Probe Microscopy: Piezoresponse Force Microscopy, Electrochemical Strain Microscopy, and Kelvin Probe Force Microscopy

ornl.gov 22/04/2019

CNMS Landing Page | ORNL

The deadline for PFM user proposal submissions for the Center for Nanophase Materials Sciences is May 1! If you have new material or are looking for nano solution for ferroelectric problem, CNMS is the place to go. The information on the full spectrum of PFM imaging and spectroscopy methods available at CNMS can be found at YouTube channel:

M*N: Microscopy, Machine Learning, Materials

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

The information on CNMS user proposals can be found at www.cnms.ornl.gov

Contact Sergei V. Kalinin directly for additional information ([email protected])

ornl.gov Overview The Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory (ORNL) provides a national and international user community access to expertise and equipment for a broad range of nanoscience research, including nanomaterials synthesis, nanofabrication, imaging/microscopy...

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Aveiro
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PES - Projeto Enriquecer Sentidos PES - Projeto Enriquecer Sentidos
Incubadora De Empresas - Universidade De Aveiro
Aveiro, 3810-193 AVEIRO

PES - Projeto Enriquecer Sentidos Atividades de Enriquecimento Curricular - AEC

Smart Connect Smart Connect
Universidade De Aveiro
Aveiro

Evento organizado pela Aveiro Smart Business que pretender conectar as várias áreas existentes da Universidade de Aveiro.

Rhum Rhum
Aveiro, 3810-183

Formação Profissional, Recrutamento e Seleção, Consultoria em Gestão Estratégica de Recursos Humanos, Workshops e Seminários Especializados.

Centro Qualif**a da Região de Aveiro Centro Qualif**a da Região de Aveiro
Rua 1º Visconde Da Granja, Nº 4
Aveiro, 3800-244

RVCC Escolar e Profissional Nível Básico e Secundário - 9º e 12º ano

Eletricidade IEFP Aveiro Eletricidade IEFP Aveiro
Cais Da Fonte Nova
Aveiro

Dicionário de Sentimentos Dicionário de Sentimentos
Aveiro

Queres dar vida aos teus sentimentos? Acede ao nosso blog e f**a a conhecer-nos melhor! Segue a nossa conta do Instagram @davidaaosteussentimentos2021 e f**a atento às novidades. Entra em contacto connosco através do [email protected]

OcularEyeCare OcularEyeCare
Avenida Dr. Francisco Sá Carneiro
Aveiro, 3810-265

Formação em Optometria Clínica e Tecnologia de Optica Ocular Consultas Optometria Clínica Exames de Diagnóstico: Topografia . Retinografia . Tonometria

Yic2012 Yic2012
Campus Universitário De Santiago
Aveiro, 3810-193 AVEIRO

YIC2012 is the first European Community on Computational Methods in Applied Sciences (ECCOMAS) Young Investigators Conference

NECO - Rede Internacional em Estudos Culturais NECO - Rede Internacional em Estudos Culturais
Aveiro, 3810-193

Bem-vindos à página do Núcleo de Estudos em Cultura e Ócio do Programa Doutoral em Estudos Culturais da Universidade de Aveiro

Companhia de Dança de Aveiro Companhia de Dança de Aveiro
Cais Dos Moliceiros, 16
Aveiro, 3810-136

Secção do GEMDA - Grupo Experimental de Música e Dança de Aveiro, associação sem fins lucrativos, criada em 1983, com o apoio da Câmara Municipal de Aveiro. Teve apoio do Estado desde 1986 até 2011 quando... um dia contaremos...

M & M - Estudos M & M - Estudos
Aveiro, 3780-130

Explicações de português-francês-inglês Apoio ao estudo Aulas para adultos Traduções Revisão de trabalhos

Tracer Tracer
Universidade De Aveiro, - Departamento De Educação, Campus Universitário De Santiago
Aveiro, 3810-193 AVEIRO

Tecnologias da Comunicação; Ensino Superior; Visualização de Informação; Universidade de Aveiro; Communication Technologies; Higher Education; Information Visualization; University of Aveiro