Search Results for author: Yongtao Liu

Found 13 papers, 9 papers with code

Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning

1 code implementation19 Apr 2024 Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop automated experiment, is emerging as a crucial objective for accelerating scientific research.

Active Learning

Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries

1 code implementation3 Feb 2024 Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov, Yongtao Liu, Rohit Pant, Xiaohang Zhang, Ichiro Takeuchi, Maxim A. Ziatdinov, Sergei V. Kalinin

This can be exemplified by the combinatorial libraries that can be explored in multiple locations by multiple tools simultaneously, or downstream characterization in automated synthesis systems.

Bayesian Optimization Dimensionality Reduction +2

Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy

1 code implementation30 Jan 2024 Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Sergei V. Kalinin

This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in Scanning Probe Microscopy.

Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy

no code implementations8 Oct 2023 Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim Ziatdinov, Rama Vasudevan

Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition.

Decision Making Dimensionality Reduction +2

A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments

1 code implementation5 Apr 2023 Arpan Biswas, Yongtao Liu, Nicole Creange, Yu-Chen Liu, Stephen Jesse, Jan-Chi Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Rama K. Vasudevan

Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites.

Active Learning Recommendation Systems

Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders

1 code implementation30 Mar 2023 Mani Valleti, Yongtao Liu, Sergei Kalinin

Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities.

Microscopy is All You Need

no code implementations12 Oct 2022 Sergei V. Kalinin, Rama Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim Ziatdinov

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods.


Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning

no code implementations30 May 2022 Maxim Ziatdinov, Yongtao Liu, Kyle Kelley, Rama Vasudevan, Sergei V. Kalinin

Recent progress in machine learning methods, and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs), have propelled automated and autonomous microscopies to the forefront of attention of the scientific community.

Active Learning Bayesian Inference +3

Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning

1 code implementation18 Mar 2022 Maxim Ziatdinov, Yongtao Liu, Sergei V. Kalinin

Active learning methods are rapidly becoming the integral component of automated experiment workflows in imaging, materials synthesis, and computation.

Active Learning

Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

1 code implementation24 May 2021 Maxim Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David Ginger, Sergei V. Kalinin

The proliferation of optical, electron, and scanning probe microscopies gives rise to large volumes of imaging data of objects as diversified as cells, bacteria, pollen, to nanoparticles and atoms and molecules.

Representation Learning Semi-Supervised Image Classification

Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy

1 code implementation20 Apr 2021 Yongtao Liu, Rama K. Vasudevan, Kyle Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim Ziatdinov

A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables.

Denoising Dimensionality Reduction

Heterochromatic nonlinear optical responses in upconversion nanoparticles for point spread function engineering

no code implementations12 Dec 2020 Chaohao Chen, Baolei Liu, Yongtao Liu, Jiayan Liao, Xuchen Shan, Fan Wang, Dayong Jin

Point spread function (PSF) engineering of the emitter can code higher spatial frequency information of an image to break diffraction limit but suffer from the complexed optical systems.


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