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Maxim A. Ziatdinov
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2020 – today
- 2024
- [j14]Mani Valleti
, Rama K. Vasudevan
Deep kernel methods learn better: from cards to process optimization. Mach. Learn. Sci. Technol. 5(1): 15012 (2024) - [i28]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) - [i27]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) - [i26]Ayana Ghosh, Maxim A. Ziatdinov, Sergei V. Kalinin:
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings. CoRR abs/2403.01234 (2024) - [i25]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) - [i24]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) - [i23]Maxim A. Ziatdinov:
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data. CoRR abs/2405.09817 (2024) - [i22]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
- [j13]Sheryl Sanchez
Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra. Adv. Intell. Syst. 5(5) (2023) - [j12]Arpan Biswas
Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach *. Mach. Learn. Sci. Technol. 4(1): 15011 (2023) - [j11]S. V. Venkatakrishnan
Adaptive sampling for accelerating neutron diffraction-based strain mapping *. Mach. Learn. Sci. Technol. 4(2): 25001 (2023) - [j10]Arpan Biswas
Combining variational autoencoders and physical bias for improved microscopy data analysis ∗. Mach. Learn. Sci. Technol. 4(4): 45004 (2023) - [j9]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) - [j8]Yongtao Liu
Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials. Patterns 4(3): 100704 (2023) - [j7]Yongtao Liu
Explainability and human intervention in autonomous scanning probe microscopy. Patterns 4(11): 100858 (2023) - [c7]Renan Souza, Tyler J. Skluzacek, Sean R. Wilkinson
Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability. e-Science 2023: 1-10 - [c6]Gayathri Saranathan
Towards Rapid Autonomous Electron Microscopy with Active Meta-Learning. SC Workshops 2023: 81-87 - [c5]Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi
Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems. SMARTCOMP 2023: 198-200 - [i21]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) - [i20]Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin:
Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis. CoRR abs/2302.04216 (2023) - [i19]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) - [i18]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) - [i17]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) - [i16]Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi, Debangshu Mukherjee, Maxim A. Ziatdinov, Craig Bridges:
Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems. CoRR abs/2307.06883 (2023) - [i15]Renan Souza, Tyler J. Skluzacek, Sean R. Wilkinson, Maxim A. Ziatdinov, Rafael Ferreira da Silva:
Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability. CoRR abs/2308.09004 (2023) - [i14]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) - 2022
- [j6]Maxim A. Ziatdinov
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process. Mach. Learn. Sci. Technol. 3(1): 15003 (2022) - [j5]Nicole Creange
Towards automating structural discovery in scanning transmission electron microscopy *. Mach. Learn. Sci. Technol. 3(1): 15024 (2022) - [j4]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) - [j3]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) - [c4]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 - [c3]Addi Malviya-Thakur
Towards a Software Development Framework for Interconnected Science Ecosystems. SMC 2022: 206-224 - [i13]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) - [i12]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) - [i11]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) - [i10]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) - [i9]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
- [j2]Yongtao Liu
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy *. Mach. Learn. Sci. Technol. 2(4): 45028 (2021) - [c2]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 - [i8]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) - [i7]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) - [i6]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) - [i5]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) - [i4]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) - [i3]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) - [i2]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
- [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) - [i1]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
- [c1]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
Coauthor Index
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last updated on 2024-11-19 20:44 CET by the dblp team
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