1 code implementation • 20 Feb 2024 • Arpan Biswas, Sai Mani Prudhvi Valleti, Rama Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often non-differentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces.
no code implementations • 8 Oct 2023 • Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim Ziatdinov, Rama Vasudevan
Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition.
1 code implementation • 17 Aug 2023 • Renan Souza, Tyler J. Skluzacek, Sean R. Wilkinson, Maxim Ziatdinov, Rafael Ferreira da Silva
Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum.
1 code implementation • 8 Feb 2023 • Arpan Biswas, Maxim Ziatdinov, Sergei V. Kalinin
Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range of structural, physical, and chemical properties of materials.
no code implementations • 12 Oct 2022 • Sergei V. Kalinin, Rama Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim Ziatdinov
We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods.
2 code implementations • 30 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.
no code implementations • 30 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.
1 code implementation • 18 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.
1 code implementation • 23 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.
1 code implementation • 24 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
1 code implementation • 16 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.
1 code implementation • 20 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.
no code implementations • 20 Apr 2021 • Maxim Ziatdinov, Sergei Kalinin
Recent advances in imaging from celestial objects in astronomy visualized via optical and radio telescopes to atoms and molecules resolved via electron and probe microscopes are generating immense volumes of imaging data, containing information about the structure of the universe from atomic to astronomic levels.
no code implementations • 21 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.
1 code implementation • 25 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
1 code implementation • 19 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
no code implementations • 2 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
no code implementations • 4 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."
2 code implementations • 27 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
1 code implementation • 9 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
no code implementations • 11 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
1 code implementation • 10 Feb 2020 • Maxim Ziatdinov, Dohyung Kim, Sabine Neumayer, Liam Collins, Mahshid Ahmadi, Rama K. Vasudevan, Stephen Jesse, Myung Hyun Ann, Jong H. Kim, Sergei V. Kalinin
Imaging mechanisms in contact Kelvin Probe Force Microscopy (cKPFM) are explored via information theory-based methods.
Applied Physics Materials Science
2 code implementations • 26 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
no code implementations • 14 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