Nature Search

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

Ayana Ghosh
Bobby G. Sumpter
Maxim Ziatdinov
ResearchOpen Access02 Jul 2021
npj Computational Materials

Volume: 7, P: 1-8
Author Correction: Atom-by-atom fabrication with electron beams

Ondrej Dyck
Maxim Ziatdinov
Sergei V. Kalinin
Amendments and Corrections20 Feb 2020
Nature Reviews Materials

Volume: 6, P: 640
Learning surface molecular structures via machine vision

Maxim Ziatdinov
Artem Maksov
Sergei V. Kalinin
ResearchOpen Access10 Aug 2017
npj Computational Materials

Volume: 3, P: 1-9
A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments

Arpan Biswas
Yongtao Liu
Rama K. Vasudevan
ResearchOpen Access03 Feb 2024
npj Computational Materials

Volume: 10, P: 1-12
Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy

Yongtao Liu
Rama K. Vasudevan
Sergei V. Kalinin
ResearchOpen Access04 Mar 2023
npj Computational Materials

Volume: 9, P: 1-8
Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

Mark P. Oxley
Maxim Ziatdinov
Sergei V. Kalinin
ResearchOpen Access10 May 2021
npj Computational Materials

Volume: 7, P: 1-6
Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

Maxim Ziatdinov
Dohyung Kim
Sergei V. Kalinin
ResearchOpen Access19 Mar 2020
npj Computational Materials

Volume: 6, P: 1-7
High-speed mapping of surface charge dynamics using sparse scanning Kelvin probe force microscopy

Dynamic mapping of charge motion across multiple length- and timescales is essential for understanding a variety of phenomena. Here, the authors introduce sparse scanning KPFM, which enables fast nanoscale charge mapping at 3 frames per second to track ion migration.

Marti Checa
Addis S. Fuhr
Liam Collins
ResearchOpen Access08 Nov 2023
Nature Communications

Volume: 14, P: 1-12
Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy

Ganesh Narasimha
Dejia Kong
Maxim Ziatdinov
ResearchOpen Access19 Jun 2025
npj Computational Materials

Volume: 11, P: 1-10
AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy

In recent years, deep learning techniques have enhanced the possibility to extract useful, high-resolution physical information from electron and scanning probe microscopy images. AtomAI, an open-source software package, can accelerate this process by bringing deep learning and simulation tools into a single framework for a range of instruments.

Maxim Ziatdinov
Ayana Ghosh
Sergei V. Kalinin
Research08 Dec 2022
Nature Machine Intelligence

Volume: 4, P: 1101-1112
Experimental discovery of structure–property relationships in ferroelectric materials via active learning

An automated workflow for scanning probe microscopy, steered by an active learning framework, can efficiently explore relationships between local domain structure and physical properties. Such a capability is demonstrated in a piezoresponse force microscopy experiment to guide measurements of ferroelectric materials.

Yongtao Liu
Kyle P. Kelley
Sergei V. Kalinin
Research04 Apr 2022
Nature Machine Intelligence

Volume: 4, P: 341-350
Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data

Ferroelectric domain wall profiles can be modeled by phenomenological Ginzburg-Landau theory, with different candidate models and parameters. Here, the authors solve the problem of model selection by developing a Bayesian inference framework allowing for uncertainty quantification and apply it to atomically resolved images of walls. This analysis can also predict the level of microscope performance needed to detect specific physical phenomena.

Christopher T. Nelson
Rama K. Vasudevan
Sergei V. Kalinin
ResearchOpen Access11 Dec 2020
Nature Communications

Volume: 11, P: 1-12
Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders

Mani Valleti
Maxim Ziatdinov
Sergei V. Kalinin
ResearchOpen Access14 Aug 2024
npj Computational Materials

Volume: 10, P: 1-19
Author Correction: Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS₂

Artem Maksov
Ondrej Dyck
Maxim Ziatdinov
Amendments and CorrectionsOpen Access04 Mar 2020
npj Computational Materials

Volume: 6, P: 1
From atomically resolved imaging to generative and causal models

High-resolution imaging methods have been instrumental in advancing our understanding of the structure of materials. To move microscopy and tomography methods forwards, approaches to reassess macroscopic concepts such as symmetry are needed.

Sergei V. Kalinin
Ayana Ghosh
Maxim Ziatdinov
Reviews29 Aug 2022
Nature Physics

Volume: 18, P: 1152-1160
Exploring electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy

Matthew G. Boebinger
Ayana Ghosh
Raymond R. Unocic
ResearchOpen Access15 Nov 2024
npj Computational Materials

Volume: 10, P: 1-10
Machine learning in scanning transmission electron microscopy

Scanning transmission electron microscopy (STEM) is a powerful tool for structural and functional imaging of materials. In this Primer, Kalinin et al. focus on the integration of machine learning and STEM to improve user experience and enhance current opportunities in STEM imaging.

Sergei V. Kalinin
Colin Ophus
Stephen J. Pennycook
Reviews03 Mar 2022
Nature Reviews Methods Primers

Volume: 2, P: 1-28
Atom-by-atom fabrication with electron beams

Electron beams in a transmission electron microscope can be used to manipulate matter atom-by-atom. This Review surveys the recent advances and discusses how the further development and integration of machine learning, theory, feedback and instrumentation will push the field forward.

Ondrej Dyck
Maxim Ziatdinov
Sergei V. Kalinin
Reviews07 Jun 2019
Nature Reviews Materials

Volume: 4, P: 497-507
Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

Rama K. Vasudevan
Maxim Ziatdinov
Sergei V. Kalinin
ReviewsOpen Access27 Jan 2021
npj Computational Materials

Volume: 7, P: 1-6
Deep Bayesian local crystallography

Sergei V. Kalinin
Mark P. Oxley
Maxim Ziatdinov
ResearchOpen Access10 Nov 2021
npj Computational Materials

Volume: 7, P: 1-12
Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Ayana Ghosh
Maxim Ziatdinov
Sergei V. Kalinin
ResearchOpen Access20 Apr 2022
npj Computational Materials

Volume: 8, P: 1-11
Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

Lukas Vlcek
Shize Yang
Sergei V. Kalinin
ResearchOpen Access25 Mar 2021
npj Computational Materials

Volume: 7, P: 1-9
Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

Christopher T. Nelson
Ayana Ghosh
Sergei V. Kalinin
ResearchOpen Access16 Sept 2021
npj Computational Materials

Volume: 7, P: 1-11
Gaussian process analysis of electron energy loss spectroscopy data: multivariate reconstruction and kernel control

Sergei V. Kalinin
Andrew R. Lupini
Maxim Ziatdinov
ResearchOpen Access28 Sept 2021
npj Computational Materials

Volume: 7, P: 1-10
Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS₂

Artem Maksov
Ondrej Dyck
Maxim Ziatdinov
ResearchOpen Access01 Feb 2019
npj Computational Materials

Volume: 5, P: 1-8
Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images

Rama K. Vasudevan
Nouamane Laanait
Sergei V. Kalinin
ResearchOpen Access28 Jun 2018
npj Computational Materials

Volume: 4, P: 1-9
Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data

Maxim Ziatdinov
Christopher T. Nelson
Sergei V. Kalinin
ResearchOpen Access21 Aug 2020
npj Computational Materials

Volume: 6, P: 1-9
Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

Artem A. Trofimov
Alison A. Pawlicki
Olga S. Ovchinnikova
ResearchOpen Access13 Jun 2019
npj Computational Materials

Volume: 5, P: 1-8
Author Correction: Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images

Rama K. Vasudevan
Nouamane Laanait
Sergei V. Kalinin
Amendments and CorrectionsOpen Access18 Dec 2018
npj Computational Materials

Volume: 4, P: 1
Computational scanning tunneling microscope image database

Kamal Choudhary
Kevin F. Garrity
Francesca Tavazza
ResearchOpen Access11 Feb 2021
Scientific Data

Volume: 8, P: 1-9
Machine learning for automated experimentation in scanning transmission electron microscopy

Sergei V. Kalinin
Debangshu Mukherjee
Steven R. Spurgeon
ReviewsOpen Access20 Dec 2023
npj Computational Materials

Volume: 9, P: 1-16

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