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 (SC, 2018)

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)

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)

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; et al. (SMC, 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)

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)

Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms. - Maxim A. Ziatdinov; 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)

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)

Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries. - Maxim A. Ziatdinov; Muammer Yusuf Yaman; Yongtao Liu; David S. Ginger; Sergei V. Kalinin (CoRR, 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)

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)

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 (e-Science, 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; et al. (SMC, 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)

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)

Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach. - Arpan Biswas; Rama K. Vasudevan; Maxim A. Ziatdinov; Sergei V. Kalinin (CoRR, 2022)

Microscopy is All You Need. - Sergei V. Kalinin; Rama K. Vasudevan; Yongtao Liu; Ayana Ghosh; Kevin Roccapriore; Maxim A. Ziatdinov (CoRR, 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)

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)

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)

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., 2022)

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability. - Renan Souza 001; Tyler J. Skluzacek; Sean R. Wilkinson; Maxim A. Ziatdinov; Rafael Ferreira da Silva (e-Science, 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 (SC Workshops, 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 (SMARTCOMP, 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)

Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis. - Arpan Biswas; Maxim A. Ziatdinov; Sergei V. Kalinin (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)

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)

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)

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., 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, 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., 2023)

Adaptive sampling for accelerating neutron diffraction-based strain mapping *. - S. V. Venkatakrishnan 001; Chris M. Fancher; Maxim A. Ziatdinov; Rama K. Vasudevan; Kyle Saleeby; James Haley; Dunji Yu; Ke An; Alex Plotkowski (Mach. Learn. Sci. Technol., 2023)

Explainability and human intervention in autonomous scanning probe microscopy. - Yongtao Liu; Maxim A. Ziatdinov; Rama K. Vasudevan; Sergei V. Kalinin (Patterns, 2023)

Combining variational autoencoders and physical bias for improved microscopy data analysis ∗. - Arpan Biswas; Maxim A. Ziatdinov; Sergei V. Kalinin (Mach. Learn. Sci. Technol., 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., 2023)

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments. - Gayathri Saranathan; Ayana Ghosh; Martin Foltin; Ann Mary Justine Koomthanam; Aalap Tripathy; Maxim A. Ziatdinov; Suparna Bhattacharya; Kevin Roccapriore; Paolo Faraboschi (SC Workshops, 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)

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)

Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings. - Ayana Ghosh; 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)

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)

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

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)

Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data. - Kamyar Barakati; Yu Liu; Chris Nelson; Maxim A. Ziatdinov; Xiaohang Zhang; Ichiro Takeuchi; Sergei V. Kalinin (CoRR, 2024)

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries. - Yu Liu; Rohit Pant; Ichiro Takeuchi; R. Jackson Spurling; Jon-Paul Maria; Maxim A. Ziatdinov; Sergei V. Kalinin (CoRR, 2024)

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization. - Arpan Biswas; Mani Valleti; Rama K. Vasudevan; Maxim A. Ziatdinov; Sergei V. Kalinin (J. Comput. Inf. Sci. Eng., 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., 2024)

Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model. - Boris N. Slautin; Utkarsh Pratiush; Doru C. Lupascu; Maxim A. Ziatdinov; Sergei V. Kalinin (CoRR, 2025)

Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy. - Aditya Vatsavai; Ganesh Narasimha; Yongtao Liu; Jan-Chi Yang; Hiroshi Funakubo; Maxim A. Ziatdinov; Rama K. Vasudevan (CoRR, 2025)

Design-to-Deployment Continuum Platform for Microscopes and Computing Ecosystems. - Anees Al-Najjar; Nageswara S. V. Rao; Ramanan Sankaran; Debangshu Mukherjee; Kevin Roccapriore; Maxim A. Ziatdinov; Sergei V. Kalinin (IEEE Trans. Ind. Informatics, 2025)

Operationalizing Serendipity: Multi-Agent AI Workflows for Enhanced Materials Characterization with Theory-in-the-Loop. - Lance Yao; Suman Samantray; Ayana Ghosh; Kevin Roccapriore; Libor Kovarik; Sarah Allec; Maxim A. Ziatdinov (CoRR, 2025)