Sergei V. Kalinin
Orcid: 0000-0001-5354-6152
According to our database1,
Sergei V. Kalinin
authored at least 61 papers
between 2015 and 2024.
Collaborative distances:
Collaborative distances:
Awards
IEEE Fellow
IEEE Fellow 2018, "For leadership in piezoresponse force microscopy for nanoscale imaging".
Timeline
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On csauthors.net:
Bibliography
2024
Mach. Learn. Sci. Technol., March, 2024
Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries.
CoRR, 2024
Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data.
CoRR, 2024
Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors.
CoRR, 2024
Rapid optimization in high dimensional space by deep kernel learning augmented genetic algorithms.
CoRR, 2024
Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments.
CoRR, 2024
Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments.
CoRR, 2024
Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy.
CoRR, 2024
Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization.
CoRR, 2024
Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope.
CoRR, 2024
Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation.
CoRR, 2024
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning.
CoRR, 2024
Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS.
CoRR, 2024
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings.
CoRR, 2024
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities.
CoRR, 2024
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries.
CoRR, 2024
Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy.
CoRR, 2024
2023
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders <sup>*</sup>.
Mach. Learn. Sci. Technol., December, 2023
Combining variational autoencoders and physical bias for improved microscopy data analysis <sup>∗</sup>.
Mach. Learn. Sci. Technol., December, 2023
Patterns, November, 2023
Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra.
Adv. Intell. Syst., May, 2023
Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials.
Patterns, March, 2023
Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach <sup>*</sup>.
Mach. Learn. Sci. Technol., March, 2023
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy.
CoRR, 2023
CoRR, 2023
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments.
CoRR, 2023
Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy.
CoRR, 2023
Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders.
CoRR, 2023
Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis.
CoRR, 2023
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space.
CoRR, 2023
2022
AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy.
Nat. Mac. Intell., December, 2022
Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model.
Mach. Learn. Sci. Technol., December, 2022
Experimental discovery of structure-property relationships in ferroelectric materials via active learning.
Nat. Mach. Intell., 2022
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process.
Mach. Learn. Sci. Technol., 2022
Towards automating structural discovery in scanning transmission electron microscopy <sup>*</sup>.
Mach. Learn. Sci. Technol., 2022
CoRR, 2022
Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach.
CoRR, 2022
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning.
CoRR, 2022
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning.
CoRR, 2022
MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.
Proceedings of the 4th Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing, 2022
Proceedings of the 18th IEEE International Conference on e-Science, 2022
2021
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy <sup>*</sup>.
Mach. Learn. Sci. Technol., 2021
Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis.
Mach. Learn. Sci. Technol., 2021
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders.
CoRR, 2021
Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries.
CoRR, 2021
AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond.
CoRR, 2021
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy.
CoRR, 2021
Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms.
CoRR, 2021
CoRR, 2021
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy.
CoRR, 2021
Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery.
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.
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.
Mach. Learn. Sci. Technol., 2020
CoRR, 2020
2018
167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation.
Proceedings of the International Conference for High Performance Computing, 2018
2017
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.
Proceedings of the International Conference on Computational Science 2016, 2016
2015
CoRR, 2015