Sergei Kalinin

Weston Fulton Chair Professor

University of Tennessee

United States

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Sergei Kalinin is the Weston Fulton Chair Professor at the University of Tennessee, USA. His research focuses on the application of artificial intelligence in materials discovery, development, and optimization, as well as causal discovery in data analysis. Recent publications highlight his work on crystalline solids, graphene dopant transitions, and the use of self-driving labs for scientific acceleration. He is also involved in exploring the adsorption of ions by ferroelectric nanoparticles and advanced functional materials.

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Collaborators (17)

Bryan Huey

Professor and Department Head

University of Connecticut

UNITED STATES

Sergei Kalinin

Research Interests

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About

Articles (30)

Reversible long-range domain wall motion in an improper ferroelectric

Reward based optimization of resonance-enhanced piezoresponse spectroscopy

Science acceleration and accessibility with self-driving labs

Artificial intelligence for advanced functional materials: exploring current and future directions

Artificial Intelligence for Materials Discovery, Development, and Optimization

Causal discovery from data assisted by large language models

Mapping causal patterns in crystalline solids

Learning and Controlling Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy

Variational Autoencoder Toolkit

Adsorption of ions from aqueous solutions by ferroelectric nanoparticles

Direct Fabrication of Atomically Defined Pores in MXenes Using Feedback‐Driven STEM

Evolution of Ferroelectric Properties in SmxBi1–xFeO3 via Automated Piezoresponse Force Microscopy across combinatorial spread libraries

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Physical discovery in representation learning via conditioning on prior knowledge

Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders

Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations

Bromine Incorporation Affects Phase Transformations and Thermal Stability of Lead Halide Perovskites

Phase diagrams and polarization reversal in nanosized HfxZr1−xO2−y

Unraveling the impact of initial choices and in-loop interventions on learning dynamics in autonomous scanning probe microscopy

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Unsupervised machine learning discovery of structural units and transformation pathways from imaging data

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders

Disentangling Electronic Transport and Hysteresis at Individual Grain Boundaries in Hybrid Perovskites via Automated Scanning Probe Microscopy

Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra

Exploring the Relationship of Microstructure and Conductivity in Metal Halide Perovskites via Active Learning-Driven Automated Scanning Probe Microscopy

Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy

Tackling overpublishing by moving to open-ended papers

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

Collaborators (17)

Bryan Huey

Aaron Merlin Müller

Mahshid Ahmadi

Boris Slautin

Manuel Zahn

John Kitchin

Susan Trolier-McKinstry

Samson A. Jenekhe

Peter Littlewood

Dennis Meier

Amanda Barnard

Yury Gogotsi

Joshua Schrier

Jonghee Yang

Thomas Lottermoser

Usha Philipose

Raymond Unocic

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