Maxim A. Ziatdinov

63 papers A 1B 5Journal 54Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation. CoRR Andrew Ritchhart, Sarah I. Allec, Pravalika Butreddy, Krista Kulesa, Qingpu Wang, Dan Thien Nguyen, Maxim A. Ziatdinov, Elias Nakouzi
2025	J	`jnl`	Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments. CoRR Ralph Bulanadi, Jawad Chowdhury, Hiroshi Funakubo, Maxim A. Ziatdinov, Rama K. Vasudevan, Arpan Biswas, Yongtao Liu
2025	J	`jnl`	Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy. CoRR Aditya Vatsavai, Ganesh Narasimha, Yongtao Liu, Jan-Chi Yang, Hiroshi Funakubo, Maxim A. Ziatdinov, Rama K. Vasudevan
2025	J	`jnl`	Design-to-Deployment Continuum Platform for Microscopes and Computing Ecosystems. IEEE Trans. Ind. Informatics Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Debangshu Mukherjee, Kevin Roccapriore, Maxim A. Ziatdinov, Sergei V. Kalinin
2025	J	`jnl`	Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model. CoRR Boris N. Slautin, Utkarsh Pratiush, Doru C. Lupascu, Maxim A. Ziatdinov, Sergei V. Kalinin
2025	J	`jnl`	Operationalizing Serendipity: Multi-Agent AI Workflows for Enhanced Materials Characterization with Theory-in-the-Loop. CoRR Lance Yao, Suman Samantray, Ayana Ghosh, Kevin Roccapriore, Libor Kovarik, Sarah I. Allec, Maxim A. Ziatdinov
2025	J	`jnl`	Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders. CoRR Boris N. Slautin, Kamyar Barakati, Hiroshi Funakubo, Maxim A. Ziatdinov, Vladimir V. Shvartsman, Doru C. Lupascu, Sergei V. Kalinin
2024	J	`jnl`	Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings. CoRR Ayana Ghosh, Maxim A. Ziatdinov, Sergei V. Kalinin
2024	—	`conf`	Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments. SC Workshops Gayathri Saranathan, Ayana Ghosh, Martin Foltin, Ann Mary Justine Koomthanam, Aalap Tripathy, Maxim A. Ziatdinov, Suparna Bhattacharya, Kevin Roccapriore, Paolo Faraboschi
2024	J	`jnl`	Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data. CoRR Maxim A. Ziatdinov
2024	J	`jnl`	Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries. CoRR Yu Liu, Rohit Pant, Ichiro Takeuchi, R. Jackson Spurling, Jon-Paul Maria, Maxim A. Ziatdinov, Sergei V. Kalinin
2024	J	`jnl`	Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning. CoRR Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
2024	J	`jnl`	Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS. CoRR Utkarsh Pratiush, Kevin M. Roccapriore, Yongtao Liu, Gerd Duscher, Maxim A. Ziatdinov, Sergei V. Kalinin
2024	J	`jnl`	Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries. CoRR Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov, Yongtao Liu, Rohit Pant, Xiaohang Zhang, Ichiro Takeuchi, Maxim A. Ziatdinov, Sergei V. Kalinin
2024	J	`jnl`	Deep kernel methods learn better: from cards to process optimization. Mach. Learn. Sci. Technol. Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
2024	J	`jnl`	Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments. CoRR Boris N. Slautin, Yu Liu, Jan Dec, Vladimir V. Shvartsman, Doru C. Lupascu, Maxim A. Ziatdinov, Sergei V. Kalinin
2024	J	`jnl`	Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors. CoRR Michael J. Kenney, Katerina G. Malollari, Sergei V. Kalinin, Maxim A. Ziatdinov
2024	J	`jnl`	Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data. CoRR Kamyar Barakati, Yu Liu, Chris Nelson, Maxim A. Ziatdinov, Xiaohang Zhang, Ichiro Takeuchi, Sergei V. Kalinin
2024	J	`jnl`	Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization. J. Comput. Inf. Sci. Eng. Arpan Biswas, Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
2024	—	`conf`	Towards FAIR Workflows for Federated Experimental Sciences. CAI Gayathri Saranathan, Martin Foltin, Aalap Tripathy, Annmary Justine, Maxim A. Ziatdinov, Ayana Ghosh, Kevin Roccapriore, Supama Bhattacharya, Paolo Faraboschi
2024	J	`jnl`	Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities. CoRR Arpan Biswas, Sai Mani Prudhvi Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
2023	J	`jnl`	A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments. CoRR Arpan Biswas, Yongtao Liu, Nicole Creange, Yu-Chen Liu, Stephen Jesse, Jan-Chi Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Rama K. Vasudevan
2023	J	`jnl`	Adaptive sampling for accelerating neutron diffraction-based strain mapping Mach. Learn. Sci. Technol. S. V. Venkatakrishnan, Chris M. Fancher, Maxim A. Ziatdinov, Rama K. Vasudevan, Kyle Saleeby, James Haley, Dunji Yu, Ke An, Alex Plotkowski
2023	J	`jnl`	Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials. Patterns Yongtao Liu, Anna N. Morozovska, Eugene A. Eliseev, Kyle P. Kelley, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
2023	J	`jnl`	Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis. CoRR Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin
2023	J	`jnl`	Combining variational autoencoders and physical bias for improved microscopy data analysis Mach. Learn. Sci. Technol. Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin
2023	B	`conf`	Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems. SMARTCOMP Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi, Debangshu Mukherjee, Maxim A. Ziatdinov, Craig Bridges
2023	J	`jnl`	Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems. CoRR Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi, Debangshu Mukherjee, Maxim A. Ziatdinov, Craig Bridges
2023	J	`jnl`	Deep Kernel Methods Learn Better: From Cards to Process Optimization. CoRR Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
2023	J	`jnl`	Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy. CoRR 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
2023	J	`jnl`	Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space. CoRR Ayana Ghosh, Sergei V. Kalinin, Maxim A. Ziatdinov
2023	J	`jnl`	Explainability and human intervention in autonomous scanning probe microscopy. Patterns Yongtao Liu, Maxim A. Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin
2023	J	`jnl`	Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra. Adv. Intell. Syst. Sheryl Sanchez, Yongtao Liu, Jonghee Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Mahshid Ahmadi
2023	J	`jnl`	Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders Mach. Learn. Sci. Technol. Maxim A. Ziatdinov, Chun Yin Wong, Sergei V. Kalinin
2023	J	`jnl`	Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy. CoRR Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim A. Ziatdinov, Rama K. Vasudevan
2023	J	`jnl`	Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach Mach. Learn. Sci. Technol. Arpan Biswas, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
2023	J	`jnl`	Probe microscopy is all you need Mach. Learn. Sci. Technol. Sergei V. Kalinin, Rama K. Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim A. Ziatdinov
2023	B	`conf`	Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability. e-Science Renan Souza, Tyler J. Skluzacek, Sean R. Wilkinson, Maxim A. Ziatdinov, Rafael Ferreira da Silva
2023	J	`jnl`	Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability. CoRR Renan Souza, Tyler J. Skluzacek, Sean R. Wilkinson, Maxim A. Ziatdinov, Rafael Ferreira da Silva
2023	—	`conf`	Towards Rapid Autonomous Electron Microscopy with Active Meta-Learning. SC Workshops Gayathri Saranathan, Martin Foltin, Aalap Tripathy, Maxim A. Ziatdinov, Ann Mary Justine Koomthanam, Suparna Bhattacharya, Ayana Ghosh, Kevin Roccapriore, Sreenivas Rangan Sukumar, Paolo Faraboschi
2022	J	`jnl`	Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning. CoRR Maxim A. Ziatdinov, Yongtao Liu, Sergei V. Kalinin
2022	J	`jnl`	AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy. Nat. Mac. Intell. Maxim A. Ziatdinov, Ayana Ghosh, Chun Yin Wong, Sergei V. Kalinin
2022	J	`jnl`	Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning. CoRR Maxim A. Ziatdinov, Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Sergei V. Kalinin
2022	B	`conf`	Enabling Autonomous Electron Microscopy for Networked Computation and Steering. e-Science Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Maxim A. Ziatdinov, Debangshu Mukherjee, Olga Ovchinnikova, Kevin Roccapriore, Andrew R. Lupini, Sergei V. Kalinin
2022	J	`jnl`	Enabling Autonomous Electron Microscopy for Networked Computation and Steering. CoRR Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Maxim A. Ziatdinov, Debangshu Mukherjee, Olga Ovchinnikova, Kevin Roccapriore, Andrew R. Lupini, Sergei V. Kalinin
2022	J	`jnl`	Experimental discovery of structure-property relationships in ferroelectric materials via active learning. Nat. Mach. Intell. Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Hiroshi Funakubo, Maxim A. Ziatdinov, Sergei V. Kalinin
2022	J	`jnl`	Microscopy is All You Need. CoRR Sergei V. Kalinin, Rama K. Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim A. Ziatdinov
2022	J	`jnl`	Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach. CoRR Arpan Biswas, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
2022	J	`jnl`	Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process. Mach. Learn. Sci. Technol. Maxim A. Ziatdinov, Ayana Ghosh, Sergei V. Kalinin
2022	B	`conf`	Towards a Software Development Framework for Interconnected Science Ecosystems. SMC 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
2022	J	`jnl`	Towards automating structural discovery in scanning transmission electron microscopy Mach. Learn. Sci. Technol. Nicole Creange, Ondrej Dyck, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
2021	J	`jnl`	AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond. CoRR Maxim A. Ziatdinov, Ayana Ghosh, Tommy Wong, Sergei V. Kalinin
2021	J	`jnl`	Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy. CoRR 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
2021	J	`jnl`	Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy Mach. Learn. Sci. Technol. Yongtao Liu, Rama K. Vasudevan, Kyle K. Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim A. Ziatdinov
2021	J	`jnl`	Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy. CoRR Yongtao Liu, Rama K. Vasudevan, Kyle P. Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim A. Ziatdinov
2021	J	`jnl`	Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy. CoRR Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck, Sergei V. Kalinin, Maxim A. Ziatdinov
2021	J	`jnl`	Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders. CoRR Maxim A. Ziatdinov, Chun Yin Wong, Sergei V. Kalinin
2021	J	`jnl`	Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms. CoRR Maxim A. Ziatdinov, Sergei V. Kalinin
2021	J	`jnl`	Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries. CoRR Maxim A. Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David S. Ginger, Sergei V. Kalinin
2021	B	`conf`	Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. SMC 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
2020	J	`jnl`	Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening. Mach. Learn. Sci. Technol. M. P. Oxley, Junqi Yin, N. Borodinov, Suhas Somnath, Maxim A. Ziatdinov, Andrew R. Lupini, Stephen Jesse, Rama K. Vasudevan, Sergei V. Kalinin
2020	J	`jnl`	Off-the-shelf deep learning is not enough: parsimony, Bayes and causality. CoRR Rama K. Vasudevan, Maxim A. Ziatdinov, Lukas Vlcek, Sergei V. Kalinin
2018	A	`conf`	167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation. SC 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