Maxim A. Ziatdinov

According to our database, Maxim A. Ziatdinov authored at least 46 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Deep kernel methods learn better: from cards to process optimization.

Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

Mach. Learn. Sci. Technol., March, 2024

Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors.

Michael J. Kenney, Katerina G. Malollari, Sergei V. Kalinin, Maxim A. Ziatdinov

CoRR, 2024

Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments.

Boris N. Slautin, Yu Liu, Jan Dec, Vladimir V. Shvartsman, Doru C. Lupascu, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2024

Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data.

Maxim A. Ziatdinov

CoRR, 2024

Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning.

Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2024

Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS.

Utkarsh Pratiush, Kevin M. Roccapriore, Yongtao Liu, Gerd Duscher, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2024

Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings.

Ayana Ghosh, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2024

Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities.

Arpan Biswas, Sai Mani Prudhvi Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2024

Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries.

Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov, Yongtao Liu, Rohit Pant, Xiaohang Zhang, Ichiro Takeuchi, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2024

2023

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

Maxim A. Ziatdinov, Chun Yin Wong, Sergei V. Kalinin

Mach. Learn. Sci. Technol., December, 2023

Combining variational autoencoders and physical bias for improved microscopy data analysis ^∗.

Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin

Mach. Learn. Sci. Technol., December, 2023

Explainability and human intervention in autonomous scanning probe microscopy.

Yongtao Liu, Maxim A. Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin

Patterns, November, 2023

Adaptive sampling for accelerating neutron diffraction-based strain mapping ^*.

S. V. Venkatakrishnan, Chris M. Fancher, Maxim A. Ziatdinov, Rama K. Vasudevan, Kyle Saleeby, James Haley, Dunji Yu, Ke An, Alex Plotkowski

Mach. Learn. Sci. Technol., June, 2023

Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra.

Sheryl Sanchez, Yongtao Liu, Jonghee Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Mahshid Ahmadi

Adv. Intell. Syst., May, 2023

Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials.

Yongtao Liu, Anna N. Morozovska, Eugene A. Eliseev, Kyle P. Kelley, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

Patterns, March, 2023

Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach ^*.

Arpan Biswas, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

Mach. Learn. Sci. Technol., March, 2023

Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy.

Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim A. Ziatdinov, Rama K. Vasudevan

CoRR, 2023

A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments.

Arpan Biswas, Yongtao Liu, Nicole Creange, Yu-Chen Liu, Stephen Jesse, Jan-Chi Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Rama K. Vasudevan

CoRR, 2023

Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy.

Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore, Ben Blaiszik, Ayana Ghosh, Maxim A. Ziatdinov, Anees Al-Najjar, Christina Doty, Sarah Akers, Nageswara S. V. Rao, Joshua C. Agar, Steven R. Spurgeon

CoRR, 2023

Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis.

Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2023

Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space.

Ayana Ghosh, Sergei V. Kalinin, Maxim A. Ziatdinov

CoRR, 2023

Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems.

Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi, Debangshu Mukherjee, Maxim A. Ziatdinov, Craig Bridges

Proceedings of the 2023 IEEE International Conference on Smart Computing, 2023

Towards Rapid Autonomous Electron Microscopy with Active Meta-Learning.

Gayathri Saranathan, Martin Foltin, Aalap Tripathy, Maxim A. Ziatdinov, Ann Mary Justine Koomthanam, Suparna Bhattacharya, Ayana Ghosh, Kevin Roccapriore, Sreenivas Rangan Sukumar, Paolo Faraboschi

Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability.

Renan Souza, Tyler J. Skluzacek, Sean R. Wilkinson, Maxim A. Ziatdinov, Rafael Ferreira da Silva

Proceedings of the 19th IEEE International Conference on e-Science, 2023

2022

AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy.

Maxim A. Ziatdinov, Ayana Ghosh, Chun Yin Wong, Sergei V. Kalinin

Nat. Mac. Intell., December, 2022

Experimental discovery of structure-property relationships in ferroelectric materials via active learning.

Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Hiroshi Funakubo, Maxim A. Ziatdinov, Sergei V. Kalinin

Nat. Mach. Intell., 2022

Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process.

Maxim A. Ziatdinov, Ayana Ghosh, Sergei V. Kalinin

Mach. Learn. Sci. Technol., 2022

Towards automating structural discovery in scanning transmission electron microscopy ^*.

Nicole Creange, Ondrej Dyck, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

Mach. Learn. Sci. Technol., 2022

Microscopy is All You Need.

Sergei V. Kalinin, Rama K. Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim A. Ziatdinov

CoRR, 2022

Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach.

Arpan Biswas, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2022

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

Maxim A. Ziatdinov, Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Sergei V. Kalinin

CoRR, 2022

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

Maxim A. Ziatdinov, Yongtao Liu, Sergei V. Kalinin

CoRR, 2022

Towards a Software Development Framework for Interconnected Science Ecosystems.

Addi Malviya-Thakur, Seth Hitefield, Marshall T. McDonnell, Matthew Wolf, Richard Archibald, Lance Drane, Kevin Roccapriore, Maxim A. Ziatdinov, Jesse McGaha, Robert Smith, John Hetrick, Mark Abraham, Sergey Yakubov, Gregory R. Watson, Ben Chance, Clara Nguyen, Matthew Baker, J. Robert Michael, Elke Arenholz, Ben Mintz

Proceedings of the Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, 2022

Enabling Autonomous Electron Microscopy for Networked Computation and Steering.

Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Maxim A. Ziatdinov, Debangshu Mukherjee, Olga Ovchinnikova, Kevin Roccapriore, Andrew R. Lupini, Sergei V. Kalinin

Proceedings of the 18th IEEE International Conference on e-Science, 2022

2021

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

Yongtao Liu, Rama K. Vasudevan, Kyle K. Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim A. Ziatdinov

Mach. Learn. Sci. Technol., 2021

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

Maxim A. Ziatdinov, Chun Yin Wong, Sergei V. Kalinin

CoRR, 2021

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

Maxim A. Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David Ginger, Sergei V. Kalinin

CoRR, 2021

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

Maxim A. Ziatdinov, Ayana Ghosh, Tommy Wong, Sergei V. Kalinin

CoRR, 2021

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

Yongtao Liu, Rama K. Vasudevan, Kyle P. Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim A. Ziatdinov

CoRR, 2021

Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms.

Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2021

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy.

Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob D. Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan

CoRR, 2021

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

Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck, Sergei V. Kalinin, Maxim A. Ziatdinov

CoRR, 2021

Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing.

Pravallika Devineni, Panchapakesan Ganesh, Nikhil Sivadas, Abhijeet Dhakane, Ketan Maheshwari, Drahomira Herrmannova, Ramakrishnan Kannan, Seung-Hwan Lim, Thomas E. Potok, Jordan B. Chipka, Priyantha Mudalige, Mark Coletti, Sajal Dash, Arnab Kumar Paul, Sarp Oral, Feiyi Wang, Bill Kay, Melissa R. Allen-Dumas, Christa Brelsford, Joshua R. New, Andy Berres, Kuldeep R. Kurte, Jibonananda Sanyal, Levi Sweet, Chathika Gunaratne, Maxim A. Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin, Olivera Kotevska, Jean C. Bilheux, Hassina Z. Bilheux, Garrett E. Granroth, Thomas Proffen, Rick Riedel, Peter F. Peterson, Shruti R. Kulkarni, Kyle P. Kelley, Stephen Jesse, Maryam Parsa

Proceedings of the Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, 2021

2020

Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening.

M. P. Oxley, Junqi Yin, N. Borodinov, Suhas Somnath, Maxim A. Ziatdinov, Andrew R. Lupini, Stephen Jesse, Rama K. Vasudevan, Sergei V. Kalinin

Mach. Learn. Sci. Technol., 2020

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

Rama K. Vasudevan, Maxim A. Ziatdinov, Lukas Vlcek, Sergei V. Kalinin

CoRR, 2020

2018

167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation.

Robert M. Patton, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Don D. March, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Thomas P. Karnowski, Maxim A. Ziatdinov, Sergei V. Kalinin

Proceedings of the International Conference for High Performance Computing, 2018