Sergei V. Kalinin

According to our database, Sergei V. Kalinin authored at least 82 papers between 2015 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments.

Boris N. Slautin, Utkarsh Pratiush, Yu Liu, Kamyar Barakati, Sergei V. Kalinin

CoRR, May, 2026

Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials.

Steven R. Spurgeon, Milad Abolhasani, Frederick Baddour, Ryan B. Comes, Vinayak P. Dravid, Hilary Egan, Patrick Emami, Robert W. Epps, Davi M. Febba, Renae Gannon, E. Ashley Gaulding, Ayana Ghosh, Kenny Gruchalla, Grace Guinan, Taro Hitosugi, Michael Holden, Sergei V. Kalinin, Yangang Liang, John S. Mangum, Matthew J. Olszta, Nathaniel H. Park, Axel Palmstrom, Michelle A. Smeaton, M. Brooks Tellekamp, Nicholas E. Thornburg, Raymond R. Unocic, Daniela Ushizima, Rama K. Vasudevan, Robert White, Andrew Young, Andriy Zakutayev

CoRR, May, 2026

PATHFINDER: Multi-objective discovery in structural and spectral spaces.

Kamyar Barakati, Boris N. Slautin, Utkarsh Pratiush, Hiroshi Funakubo, Sergei V. Kalinin

CoRR, April, 2026

AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design.

Utkarsh Pratiush, Huaixun Huyan, Maryam Zahiri Azar, Esmeralda Yitamben, Allen Bourez, Sergei V. Kalinin, Vasfi Burak Ozdol

CoRR, April, 2026

Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy.

Utkarsh Pratiush, Kamyar Barakati, Boris N. Slautin, Catherine C. Bodinger, Christopher D. Lowe, Brandi M. Cossairt, Sergei V. Kalinin

CoRR, March, 2026

Towards Self-Optimizing Electron Microscope: Robust Tuning of Aberration Coefficients via Physics-Aware Multi-Objective Bayesian Optimization.

Utkarsh Pratiush, Austin Houston, Richard Liu, Gerd Duscher, Sergei V. Kalinin

CoRR, January, 2026

Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs.

Kamyar Barakati, Haochen Zhu, C. Charlotte Buchanan, Dustin A. Gilbert, Philip D. Rack, Sergei V. Kalinin

CoRR, January, 2026

2025

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions.

Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price, Victor Posligua, Stephan Roche, O. Anatole von Lilienfeld, Konstantin S. Novoselov, Xavier Bresson, Gianmarco Mengaldo, Xudong Chen, Terence J. O'Kane, Emily R. Lines, Matthew J. Allen, Amandine E. Debus, Clayton Miller, Jiayu Zhou, Hiroko H. Dodge, David Rousseau, Andrey Ustyuzhanin, Ziyun Yan, Mario Lanza, Fabio Sciarrino, Ryo Yoshida, Zhidong Leong, Teck Leong Tan, Qianxiao Li, Adil Kabylda, Igor Poltavsky, Alexandre Tkatchenko, Sherif Abdulkader Tawfik, Prathami Divakar Kamath, Théo Jaffrelot Inizan, Kristin A. Persson, Bryant Y. Li, Vir Karan, Chenru Duan, Haojun Jia, Qiyuan Zhao, Hiroyuki Hayashi, Atsuto Seko, Isao Tanaka, Omar M. Yaghi, Tim Gould, Bun Chan, Stefan Vuckovic, Tianbo Li, Min Lin, Zehcen Tang, Yang Li, Yong Xu, Amrita Joshi, Xiaonan Wang, Leonard W. T. Ng, Sergei V. Kalinin, Mahshid Ahmadi, Jiyizhe Zhang, Shuyuan Zhang, Alexei A. Lapkin, Ming Xiao, Zhe Wu, Kedar Hippalgaonkar, Limsoon Wong, Lorenzo Bastonero, Nicola Marzari, Dorye Luis Esteras Cordoba, Andrei Tomut, Alba Quinones Andrade, Jose-Hugo Garcia

CoRR, November, 2025

DIVIDE: A Framework for Learning from Independent Multi-Mechanism Data Using Deep Encoders and Gaussian Processes.

Vivek Chawla, Boris N. Slautin, Utkarsh Pratiush, Dayakar Penumadu, Sergei V. Kalinin

CoRR, November, 2025

Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders.

Boris N. Slautin, Kamyar Barakati, Hiroshi Funakubo, Maxim A. Ziatdinov, Vladimir V. Shvartsman, Doru C. Lupascu, Sergei V. Kalinin

CoRR, October, 2025

SAM^*: Task-Adaptive SAM with Physics-Guided Rewards.

Kamyar Barakati, Utkarsh Pratiush, Sheryl Sanchez, Aditya Raghavan, Delia J. Milliron, Mahshid Ahmadi, Philip D. Rack, Sergei V. Kalinin

CoRR, September, 2025

Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy.

Yu Liu, Utkarsh Pratiush, Kamyar Barakati, Hiroshi Funakubo, Ching-Che Lin, Jaegyu Kim, Lane W. Martin, Sergei V. Kalinin

CoRR, June, 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, May, 2025

Exploring Domain Wall Pinning in Ferroelectrics via Automated High Throughput AFM.

Kamyar Barakati, Yu Liu, Hiroshi Funakubo, Sergei V. Kalinin

CoRR, May, 2025

The Power of the Pareto Front: Balancing Uncertain Rewards for Adaptive Experimentation in scanning probe microscopy.

Yu Liu, Sergei V. Kalinin

CoRR, April, 2025

Causal Discovery from Data Assisted by Large Language Models.

Kamyar Barakati, Alexander Molak, Chris Nelson, Xiaohang Zhang, Ichiro Takeuchi, Sergei V. Kalinin

CoRR, March, 2025

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, March, 2025

Rewards-based image analysis in microscopy.

Kamyar Barakati, Yu Liu, Utkarsh Pratiush, Boris N. Slautin, Sergei V. Kalinin

CoRR, February, 2025

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy.

Utkarsh Pratiush, Austin Houston, Kamyar Barakati, Aditya Raghavan, Ralph Bulanadi, Xiangyu Yin, Samuel S. Welborn, Dasol Yoon, Harikrishnan KP, Zhaslan Baraissov, Desheng Ma, Mikolaj Jakowski, Shawn-Patrick Barhorst, Alexander J. Pattison, Panayotis Manganaris, Sita Sirisha Madugula, Sai Venkata Gayathri Ayyagari, Vishal Kennedy, Michelle Wang, Kieran J. Pang, Ian Addison-Smith, Willy Menacho, Horacio V. Guzman, Alexander Kiefer, Nicholas Furth, Nikola L. Kolev, Mikhail Petrov, Viktoriia Liu, Sergey Ilyev, Srikar Rairao, Tommaso Rodani, Ivan Pinto-Huguet, Xuli Chen, Josep Cruañes, Marta Torrens, Jovan Pomar, Fanzhi Su, Pawan Vedanti, Zhiheng Lyu, Xingzhi Wang, Lehan Yao, Amir Taqieddin, Forrest Laskowski, Yu-Tsun Shao, Benjamin Fein-Ashley, Yi Jiang, Vineet Kumar, Himanshu Mishra, Yogesh Paul, Adib Bazgir, Rama Chandra Praneeth Madugula, Yuwen Zhang, Pravan Omprakash, Jian Huang, Eric Montufar-Morales, Vivek Chawla, Harshit Sethi, Jie Huang, Lauri Kurki, Grace Guinan, Addison Salvador, Arman H. Ter-Petrosyan, Madeline Van Winkle, Steven R. Spurgeon, Ganesh Narasimha, Zijie Wu, Richard Liu, Yongtao Liu, Boris N. Slautin, Andrew R. Lupini, Rama K. Vasudevan, Gerd Duscher, Sergei V. Kalinin

Mach. Learn. Sci. Technol., 2025

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

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

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

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

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

Rapid optimization in high dimensional space by deep kernel learning augmented genetic algorithms.

Mani Valleti, Aditya Raghavan, Sergei V. Kalinin

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

Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments.

Kamyar Barakati, Utkarsh Pratiush, Austin Houston, Gerd Duscher, Sergei V. Kalinin

CoRR, 2024

Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy.

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

CoRR, 2024

Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization.

Aditya Raghavan, Utkarsh Pratiush, Mani Valleti, Richard Liu, Reece Emery, Hiroshi Funakubo, Yongtao Liu, Philip D. Rack, Sergei V. Kalinin

CoRR, 2024

Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope.

Utkarsh Pratiush, Austin Houston, Sergei V. Kalinin, Gerd Duscher

CoRR, 2024

Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation.

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

CoRR, 2024

Physics-based reward driven image analysis in microscopy.

Kamyar Barakati, Hui Yuan, Amit Goyal, 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

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

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

Ayana Ghosh, 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

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

Probe microscopy is all you need ^*.

Sergei V. Kalinin, Rama K. Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim A. Ziatdinov

Mach. Learn. Sci. Technol., June, 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 S. 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