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ORCIDMaxim A. Ziatdinov
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2020 – today
- 2024
[j14]Mani Valleti
, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin:
Deep kernel methods learn better: from cards to process optimization. Mach. Learn. Sci. Technol. 5(1): 15012 (2024)- [i29]Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov, Yongtao Liu, Rohit Pant, Xiaohang Zhang, Ichiro Takeuchi, Maxim A. Ziatdinov, Sergei V. Kalinin:
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries. CoRR abs/2402.02198 (2024) - [i28]Arpan Biswas, Sai Mani Prudhvi Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin:
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities. CoRR abs/2402.13402 (2024) - [i27]Ayana Ghosh, Maxim A. Ziatdinov, Sergei V. Kalinin:
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings. CoRR abs/2403.01234 (2024) - [i26]Utkarsh Pratiush, Kevin M. Roccapriore, Yongtao Liu, Gerd Duscher, Maxim A. Ziatdinov, Sergei V. Kalinin:
Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS. CoRR abs/2404.07381 (2024) - [i25]Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin:
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning. CoRR abs/2404.12899 (2024) - [i24]Maxim A. Ziatdinov:
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data. CoRR abs/2405.09817 (2024) - [i23]Boris N. Slautin, Yu Liu, Jan Dec, Vladimir V. Shvartsman, Doru C. Lupascu, Maxim A. Ziatdinov, Sergei V. Kalinin:
Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments. CoRR abs/2410.02717 (2024) - [i22]Michael J. Kenney, Katerina G. Malollari, Sergei V. Kalinin, Maxim A. Ziatdinov:
Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors. CoRR abs/2410.06422 (2024) - 2023
- [j13]Sheryl Sanchez, Yongtao Liu, Jonghee Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Mahshid Ahmadi:
Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra. Adv. Intell. Syst. 5(5) (2023) - [j12]Arpan Biswas, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin:
Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach *. Mach. Learn. Sci. Technol. 4(1): 15011 (2023) - [j11]S. V. Venkatakrishnan, Chris M. Fancher, Maxim A. Ziatdinov, Rama K. Vasudevan, Kyle Saleeby, James Haley, Dunji Yu, Ke An, Alex Plotkowski:
Adaptive sampling for accelerating neutron diffraction-based strain mapping *. Mach. Learn. Sci. Technol. 4(2): 25001 (2023) - [j10]Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin:
Combining variational autoencoders and physical bias for improved microscopy data analysis ∗. Mach. Learn. Sci. Technol. 4(4): 45004 (2023) - [j9]Maxim A. Ziatdinov, Chun Yin Wong, Sergei V. Kalinin:
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *. Mach. Learn. Sci. Technol. 4(4): 45033 (2023) - [j8]Yongtao Liu, Anna N. Morozovska, Eugene A. Eliseev, Kyle P. Kelley, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin:
Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials. Patterns 4(3): 100704 (2023) - [j7]Yongtao Liu, Maxim A. Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin:
Explainability and human intervention in autonomous scanning probe microscopy. Patterns 4(11): 100858 (2023) - [c7]Renan Souza, Tyler J. Skluzacek, Sean R. Wilkinson, Maxim A. Ziatdinov, Rafael Ferreira da Silva:
Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability. e-Science 2023: 1-10 - [c6]Gayathri Saranathan, Martin Foltin, Aalap Tripathy, Maxim A. Ziatdinov, Ann Mary Justine Koomthanam, Suparna Bhattacharya, Ayana Ghosh, Kevin Roccapriore, Sreenivas Rangan Sukumar, Paolo Faraboschi:
Towards Rapid Autonomous Electron Microscopy with Active Meta-Learning. SC Workshops 2023: 81-87 - [c5]Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi, Debangshu Mukherjee, Maxim A. Ziatdinov, Craig Bridges:
Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems. SMARTCOMP 2023: 198-200 - [i21]Ayana Ghosh, Sergei V. Kalinin, Maxim A. Ziatdinov:
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space. CoRR abs/2301.02665 (2023) - [i20]Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin:
Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis. CoRR abs/2302.04216 (2023) - [i19]Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin:
Deep Kernel Methods Learn Better: From Cards to Process Optimization. CoRR abs/2303.14554 (2023) - [i18]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:
Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy. CoRR abs/2304.02048 (2023) - [i17]Arpan Biswas, Yongtao Liu, Nicole Creange, Yu-Chen Liu, Stephen Jesse, Jan-Chi Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Rama K. Vasudevan:
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments. CoRR abs/2304.02484 (2023) - [i16]Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi, Debangshu Mukherjee, Maxim A. Ziatdinov, Craig Bridges:
Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems. CoRR abs/2307.06883 (2023) - [i15]Renan Souza, Tyler J. Skluzacek, Sean R. Wilkinson, Maxim A. Ziatdinov, Rafael Ferreira da Silva:
Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability. CoRR abs/2308.09004 (2023) - [i14]Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim A. Ziatdinov, Rama K. Vasudevan:
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy. CoRR abs/2310.05018 (2023) - 2022
- [j6]Maxim A. Ziatdinov, Ayana Ghosh, Sergei V. Kalinin:
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process. Mach. Learn. Sci. Technol. 3(1): 15003 (2022) - [j5]Nicole Creange, Ondrej Dyck, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin:
Towards automating structural discovery in scanning transmission electron microscopy *. Mach. Learn. Sci. Technol. 3(1): 15024 (2022) - [j4]Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Hiroshi Funakubo, Maxim A. Ziatdinov, Sergei V. Kalinin:
Experimental discovery of structure-property relationships in ferroelectric materials via active learning. Nat. Mach. Intell. 4(4): 341-350 (2022) - [j3]Maxim A. Ziatdinov, Ayana Ghosh, Chun Yin Wong, Sergei V. Kalinin:
AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy. Nat. Mac. Intell. 4(12): 1101-1112 (2022) - [c4]Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Maxim A. Ziatdinov, Debangshu Mukherjee, Olga Ovchinnikova, Kevin Roccapriore, Andrew R. Lupini, Sergei V. Kalinin:
Enabling Autonomous Electron Microscopy for Networked Computation and Steering. e-Science 2022: 267-277 - [c3]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:
Towards a Software Development Framework for Interconnected Science Ecosystems. SMC 2022: 206-224 - [i13]Maxim A. Ziatdinov, Yongtao Liu, Sergei V. Kalinin:
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning. CoRR abs/2203.10181 (2022) - [i12]Maxim A. Ziatdinov, Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Sergei V. Kalinin:
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning. CoRR abs/2205.15458 (2022) - [i11]Arpan Biswas, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin:
Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach. CoRR abs/2207.00128 (2022) - [i10]Sergei V. Kalinin, Rama K. Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim A. Ziatdinov:
Microscopy is All You Need. CoRR abs/2210.06526 (2022) - [i9]Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Maxim A. Ziatdinov, Debangshu Mukherjee, Olga Ovchinnikova, Kevin Roccapriore, Andrew R. Lupini, Sergei V. Kalinin:
Enabling Autonomous Electron Microscopy for Networked Computation and Steering. CoRR abs/2210.09791 (2022) - 2021
- [j2]Yongtao Liu, Rama K. Vasudevan, Kyle K. Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim A. Ziatdinov:
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy *. Mach. Learn. Sci. Technol. 2(4): 45028 (2021) - [c2]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:
Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. SMC 2021: 361-382 - [i8]Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck, Sergei V. Kalinin, Maxim A. Ziatdinov:
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy. CoRR abs/2101.08449 (2021) - [i7]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:
Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy. CoRR abs/2103.12165 (2021) - [i6]Maxim A. Ziatdinov, Sergei V. Kalinin:
Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms. CoRR abs/2104.10180 (2021) - [i5]Yongtao Liu, Rama K. Vasudevan, Kyle P. Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim A. Ziatdinov:
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy. CoRR abs/2104.10207 (2021) - [i4]Maxim A. Ziatdinov, Ayana Ghosh, Tommy Wong, Sergei V. Kalinin:
AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond. CoRR abs/2105.07485 (2021) - [i3]Maxim A. Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David Ginger, Sergei V. Kalinin:
Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries. CoRR abs/2105.11475 (2021) - [i2]Maxim A. Ziatdinov, Chun Yin Wong, Sergei V. Kalinin:
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders. CoRR abs/2106.12472 (2021) - 2020
- [j1]M. P. Oxley, Junqi Yin, N. Borodinov, Suhas Somnath, Maxim A. Ziatdinov, Andrew R. Lupini, Stephen Jesse, Rama K. Vasudevan, Sergei V. Kalinin:
Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening. Mach. Learn. Sci. Technol. 1(4): 04 (2020) - [i1]Rama K. Vasudevan, Maxim A. Ziatdinov, Lukas Vlcek, Sergei V. Kalinin:
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality. CoRR abs/2005.01557 (2020)
2010 – 2019
- 2018
- [c1]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:
167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation. SC 2018: 50:1-50:11
Coauthor Index
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