Search Results for author: Sergei V. Kalinin

Found 25 papers, 15 papers with code

Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach

no code implementations30 Jun 2022 Arpan Biswas, Rama Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin

Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find latent manifolds for classification and regression of complex experimental data.

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

no code implementations30 May 2022 Maxim Ziatdinov, Yongtao Liu, Kyle Kelley, Rama Vasudevan, Sergei V. Kalinin

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.

Active Learning Bayesian Inference +3

Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning

1 code implementation18 Mar 2022 Maxim Ziatdinov, Yongtao Liu, Sergei V. Kalinin

Active learning methods are rapidly becoming the integral component of automated experiment workflows in imaging, materials synthesis, and computation.

Active Learning

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders

1 code implementation23 Jun 2021 Maxim Ziatdinov, Chun Yin Wong, Sergei V. Kalinin

Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials.

Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

1 code implementation24 May 2021 Maxim Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David Ginger, Sergei V. Kalinin

The proliferation of optical, electron, and scanning probe microscopies gives rise to large volumes of imaging data of objects as diversified as cells, bacteria, pollen, to nanoparticles and atoms and molecules.

Representation Learning Semi-Supervised Image Classification

AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond

1 code implementation16 May 2021 Maxim Ziatdinov, Ayana Ghosh, Tommy Wong, Sergei V. Kalinin

AtomAI is an open-source software package bridging instrument-specific Python libraries, deep learning, and simulation tools into a single ecosystem.

Disentanglement Ensemble Learning +5

Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy

1 code implementation20 Apr 2021 Yongtao Liu, Rama K. Vasudevan, Kyle Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim Ziatdinov

A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables.

Denoising Dimensionality Reduction

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy

no code implementations22 Mar 2021 Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis.

Autonomous Driving Decision Making +1

Mapping causal patterns in crystalline solids

no code implementations2 Mar 2021 Chris Nelson, Anna N. Morozovska, Maxim A. Ziatdinov, Eugene A. Eliseev, Xiaohang Zhang, Ichiro Takeuchi, Sergei V. Kalinin

The evolution of the atomic structures of the combinatorial library of Sm-substituted thin film BiFeO3 along the phase transition boundary from the ferroelectric rhombohedral phase to the non-ferroelectric orthorhombic phase is explored using scanning transmission electron microscopy (STEM).

Data Analysis, Statistics and Probability Materials Science

Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy

no code implementations21 Jan 2021 Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck, Sergei V. Kalinin, Maxim Ziatdinov

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest.

Ensemble Learning Transfer Learning

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

1 code implementation25 Nov 2020 Rama K. Vasudevan, Kyle Kelley, Hiroshi Funakubo, Stephen Jesse, Sergei V. Kalinin, Maxim Ziatdinov

Polarization dynamics in ferroelectric materials are explored via the automated experiment in Piezoresponse Force Spectroscopy.

Disordered Systems and Neural Networks Data Analysis, Statistics and Probability

Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data

1 code implementation19 Oct 2020 Sergei V. Kalinin, Ondrej Dyck, Ayana Ghosh, Bobby G. Sumpter, Maxim Ziatdinov

We show that unsupervised machine learning can be used to learn physical and chemical transformation pathways from the observational microscopic data, as demonstrated for atomically resolved images in Scanning Transmission Electron Microscopy (STEM) and ferroelectric domain structures in Piezoresponse Force Microscopy (PFM).

Semantic Segmentation Materials Science

Exploring particle dynamics during self-organization processes via rotationally invariant latent representations

no code implementations2 Sep 2020 Sergei V. Kalinin, Shuai Zhang, Mani Valleti, Harley Pyles, David Baker, James J. De Yoreo, Maxim Ziatdinov

The dynamic of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution.

Soft Condensed Matter

The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design

2 code implementations3 Jul 2020 Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei V. Kalinin, Bobby G. Sumpter, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe, Francesca Tavazza

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.

Materials Science Computational Physics

Off-the-shelf deep learning is not enough: parsimony, Bayes and causality

no code implementations4 May 2020 Rama K. Vasudevan, Maxim Ziatdinov, Lukas Vlcek, Sergei V. Kalinin

Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level performance and ushered in a new wave of "AI."

speech-recognition Speech Recognition

Guided search for desired functional responses via Bayesian optimization of generative model: hysteresis loop shape engineering in ferroelectrics

2 code implementations27 Apr 2020 Sergei V. Kalinin, Maxim Ziatdinov, Rama K. Vasudevan

Here we suggest and implement a Gaussian Process based methods that allow to effectively sample the degenerate parameter space of a complex non-local model to output regions of parameter space which yield desired functionalities.

Disordered Systems and Neural Networks Materials Science Computational Physics

Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian Process-based Exploration-Exploitation

1 code implementation9 Apr 2020 Sergei V. Kalinin, Mani Valleti, Rama K. Vasudevan, Maxim Ziatdinov

Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics, that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase-separated materials to quantum systems.

Materials Science Computational Physics

Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution Scanning Transmission Electron Microscopy data

no code implementations11 Feb 2020 Maxim Ziatdinov, Chris Nelson, Xiaohang Zhang, Rama Vasudevan, Eugene Eliseev, Anna N. Morozovska, Ichiro Takeuchi, Sergei V. Kalinin

Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy, with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.

Materials Science

Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process Modelling

2 code implementations26 Nov 2019 Maxim Ziatdinov, Dohyung Kim, Sabine Neumayer, Rama K. Vasudevan, Liam Collins, Stephen Jesse, Mahshid Ahmadi, Sergei V. Kalinin

We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods.

Computational Physics Materials Science

USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data

1 code implementation22 Mar 2019 Suhas Somnath, Chris R. Smith, Nouamane Laanait, Rama K. Vasudevan, Anton Ievlev, Alex Belianinov, Andrew R. Lupini, Mallikarjun Shankar, Sergei V. Kalinin, Stephen Jesse

The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID.

Data Analysis, Statistics and Probability

Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy

1 code implementation18 Oct 2018 Xin Li, Ondrej E. Dyck, Mark P. Oxley, Andrew R. Lupini, Leland McInnes, John Healy, Stephen Jesse, Sergei V. Kalinin

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields.

Compressed Sensing of Scanning Transmission Electron Microscopy (STEM) on Non-Rectangular Scans

1 code implementation13 May 2018 Xin Li, Ondrej Dyck, Sergei V. Kalinin, Stephen Jesse

Scanning Transmission Electron Microscopy (STEM) has become the main stay for materials characterization on atomic level, with applications ranging from visualization of localized and extended defects to mapping order parameter fields.

Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2

no code implementations14 Mar 2018 Artem Maksov, Ondrej Dyck, Kai Wang, Kai Xiao, David B. Geohegan, Bobby G. Sumpter, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin, Maxim Ziatdinov

Understanding elementary mechanisms behind solid-state phase transformations and reactions is the key to optimizing desired functional properties of many technologically relevant materials.

Materials Science

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