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Sergei V. Kalinin
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2020 – today
- 2024
- [j16]Mani Valleti
, Rama K. Vasudevan
Deep kernel methods learn better: from cards to process optimization. Mach. Learn. Sci. Technol. 5(1): 15012 (2024) - [i38]Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Sergei V. Kalinin:
Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy. CoRR abs/2402.00071 (2024) - [i37]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) - [i36]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) - [i35]Ayana Ghosh, Maxim A. Ziatdinov, Sergei V. Kalinin:
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings. CoRR abs/2403.01234 (2024) - [i34]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) - [i33]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) - [i32]Kamyar Barakati, Hui Yuan, Amit Goyal, Sergei V. Kalinin:
Physics-based reward driven image analysis in microscopy. CoRR abs/2404.14146 (2024) - [i31]Yu Liu, Utkarsh Pratiush, Jason Bemis, Roger Proksch, Reece Emery, Philip D. Rack, Yu-Chen Liu, Jan-Chi Yang, Stanislav Udovenko, Susan Trolier-McKinstry, Sergei V. Kalinin:
Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation. CoRR abs/2405.12300 (2024) - [i30]Utkarsh Pratiush, Austin Houston, Sergei V. Kalinin, Gerd Duscher:
Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope. CoRR abs/2406.11018 (2024) - [i29]Aditya Raghavan, Utkarsh Pratiush, Mani Valleti, Richard Liu, Reece Emery, Hiroshi Funakubo, Yongtao Liu, Philip D. Rack, Sergei V. Kalinin:
Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization. CoRR abs/2408.00229 (2024) - [i28]Yu Liu, RogerProksch, Jason Bemis, Utkarsh Pratiush, Astita Dubey, Mahshid Ahmadi, Reece Emery, Philip D. Rack, Yu-Chen Liu, Jan-Chi Yang, Sergei V. Kalinin:
Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy. CoRR abs/2408.04055 (2024) - [i27]Kamyar Barakati, Utkarsh Pratiush, Austin Houston, Gerd Duscher, Sergei V. Kalinin:
Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments. CoRR abs/2409.12462 (2024) - [i26]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) - [i25]Mani Valleti, Aditya Raghavan, Sergei V. Kalinin:
Rapid optimization in high dimensional space by deep kernel learning augmented genetic algorithms. CoRR abs/2410.03173 (2024) - [i24]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
- [j15]Sheryl Sanchez
Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra. Adv. Intell. Syst. 5(5) (2023) - [j14]Arpan Biswas
Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach *. Mach. Learn. Sci. Technol. 4(1): 15011 (2023) - [j13]Arpan Biswas
Combining variational autoencoders and physical bias for improved microscopy data analysis ∗. Mach. Learn. Sci. Technol. 4(4): 45004 (2023) - [j12]Maxim A. Ziatdinov
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *. Mach. Learn. Sci. Technol. 4(4): 45033 (2023) - [j11]Yongtao Liu
Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials. Patterns 4(3): 100704 (2023) - [j10]Yongtao Liu
Explainability and human intervention in autonomous scanning probe microscopy. Patterns 4(11): 100858 (2023) - [i23]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) - [i22]Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin:
Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis. CoRR abs/2302.04216 (2023) - [i21]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) - [i20]Mani Valleti, Yongtao Liu, Sergei V. Kalinin:
Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders. CoRR abs/2303.18236 (2023) - [i19]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) - [i18]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) - [i17]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) - [i16]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:
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. CoRR abs/2311.17894 (2023) - 2022
- [j9]Maxim A. Ziatdinov
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process. Mach. Learn. Sci. Technol. 3(1): 15003 (2022) - [j8]Nicole Creange
Towards automating structural discovery in scanning transmission electron microscopy *. Mach. Learn. Sci. Technol. 3(1): 15024 (2022) - [j7]Rama K. Vasudevan
Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model. Mach. Learn. Sci. Technol. 3(4): 4 (2022) - [j6]Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Hiroshi Funakubo, Maxim A. Ziatdinov
Experimental discovery of structure-property relationships in ferroelectric materials via active learning. Nat. Mach. Intell. 4(4): 341-350 (2022) - [j5]Maxim A. Ziatdinov
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) - [c7]Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Maxim A. Ziatdinov, Debangshu Mukherjee, Olga Ovchinnikova, Kevin Roccapriore, Andrew R. Lupini
Enabling Autonomous Electron Microscopy for Networked Computation and Steering. e-Science 2022: 267-277 - [c6]Zhuowen Zhao
MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies. XLOOP@SC 2022: 10-15 - [i15]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) - [i14]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) - [i13]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) - [i12]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:
MLExchange: A web-based platform enabling exchangeable machine learning workflows. CoRR abs/2208.09751 (2022) - [i11]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) - [i10]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
- [j4]Nicole Creange
Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis. Mach. Learn. Sci. Technol. 2(4): 45002 (2021) - [j3]Yongtao Liu
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy *. Mach. Learn. Sci. Technol. 2(4): 45028 (2021) - [c5]Suhas Somnath, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin
Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery. SMC 2021: 58-75 - [c4]Pravallika Devineni, Panchapakesan Ganesh, Nikhil Sivadas, Abhijeet Dhakane
Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. SMC 2021: 361-382 - [i9]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) - [i8]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) - [i7]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) - [i6]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) - [i5]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) - [i4]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) - [i3]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
- [j2]Xin Li
Estimating Preisach Density via Subset Selection. IEEE Access 8: 61767-61774 (2020) - [j1]M. P. Oxley, Junqi Yin, N. Borodinov, Suhas Somnath, Maxim A. Ziatdinov, Andrew R. Lupini
Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening. Mach. Learn. Sci. Technol. 1(4): 04 (2020) - [i2]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
- [c3]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 - 2017
- [c2]Patrick Ponath, Agham B. Posadas, Yuan Ren, Xiaoyu Wu, Keji Lai, Alex Demkov, Michael Schmidt, Ray Duffy
Advances of the development of a ferroelectric field-effect transistor on Ge(001). ICICDT 2017: 1-3 - 2016
- [c1]Eric J. Lingerfelt, Alex Belianinov, Eirik Endeve
BEAM: A Computational Workflow System for Managing and Modeling Material Characterization Data in HPC Environments. ICCS 2016: 2276-2280 - 2015
- [i1]Sergei V. Kalinin, Artem Maksov:
What makes us a community: structure, correlations, and success in scientific world. CoRR abs/1502.03439 (2015)
Coauthor Index
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