Sergei V. Kalinin

According to our database, Sergei V. Kalinin authored at least 48 papers between 2015 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

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

Ayana Ghosh, Maxim Ziatdinov and, 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

Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy.

Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, 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

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

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy.

Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron C. Courville, Marc G. Bellemare, Sergei V. Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore

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

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

Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders.

Mani Valleti, Yongtao Liu, Sergei V. Kalinin

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

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

Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model.

Rama K. Vasudevan, Erick Orozco, Sergei V. Kalinin

Mach. Learn. Sci. Technol., 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

MLExchange: A web-based platform enabling exchangeable machine learning workflows.

Zhuowen Zhao, Tanny Chavez, Elizabeth Holman, Guanhua Hao, Adam Green, Harinarayan Krishnan, Dylan McReynolds, Ronald Pandolfi, Eric J. Roberts, Petrus H. Zwart, Howard Yanxon, Nicholas Schwarz, Subramanian Sankaranarayanan, Sergei V. Kalinin, Apurva Mehta, Stuart Campbell, Alexander Hexemer

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

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.

Zhuowen Zhao, Tanny Chavez, Elizabeth Holman, Guanhua Hao, Adam Green, Harinarayan Krishnan, Dylan McReynolds, Ronald Pandolfi, Eric J. Roberts, Petrus H. Zwart, Howard Yanxon, Nicholas Schwarz, Subramanian Sankaranarayanan, Sergei V. Kalinin, Apurva Mehta, Stuart Campbell, Alexander Hexemer

Proceedings of the 4th Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing, 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

Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis.

Nicole Creange, Kyle P. Kelley, C. Smith, D. Sando, Oliver Paull, N. Valanoor, S. Somnath, S. Jesse, Sergei V. Kalinin, Rama K. Vasudevan

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

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery.

Suhas Somnath, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin, Nageswara S. V. Rao, Christopher Brumgard, Feiyi Wang, Olga A. Kuchar, Arjun Shankar, Ben Mintz, Elke Arenholz, J. Robert Michael, Sarp Oral

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

Estimating Preisach Density via Subset Selection.

Xin Li, Dohyung Kim, Sabine M. Neumayer, Mahshid Ahmadi, Sergei V. Kalinin

IEEE Access, 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

2017

Advances of the development of a ferroelectric field-effect transistor on Ge(001).

Patrick Ponath, Agham B. Posadas, Yuan Ren, Xiaoyu Wu, Keji Lai, Alex Demkov, Michael Schmidt, Ray Duffy, Paul K. Hurley, Jian Wang, Chadwin D. Young, Rama K. Vasudevan, M. Baris Okatan, Stephen Jesse, Sergei V. Kalinin

Proceedings of the 2017 IEEE International Conference on IC Design and Technology, 2017

2016

BEAM: A Computational Workflow System for Managing and Modeling Material Characterization Data in HPC Environments.

Eric J. Lingerfelt, Alex Belianinov, Eirik Endeve, O. Ovchinnikov, Suhas Somnath, Jose M. Borreguero, N. Grodowitz, B. Park, Richard K. Archibald, Christopher T. Symons, Sergei V. Kalinin, O. E. Bronson Messer, Mallikarjun Shankar, Stephen Jesse

Proceedings of the International Conference on Computational Science 2016, 2016

2015

What makes us a community: structure, correlations, and success in scientific world.

Sergei V. Kalinin, Artem Maksov

CoRR, 2015