Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Atomic Force Microscopy

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Biodegradable Acoustic Targeting for Ultrasound-Supported Gene Therapy (BATUS) in Glioblastoma.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Response of Spinal Cord Blood Flow to Hypotensive and Adrenergic Challenges: Doppler Ultrasound of the Porcine Sulcal Artery.

Neurosurgery·2026

Same author

Correlation between tissue temperature and ablation interval time under ultra-high-power short-duration ablation: Ex vivo porcine model.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing·2026

Same author

Biologic immunomodulators and spinal fusion: evidence, mechanisms, and perioperative considerations.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026

Same author

Strong ultrafast nonlinear optical response from megaelectronvolt electrons in semiconductors.

Nature photonics·2026

Same author

Berry pseudorotation enabled photodissociation of Fe(CO)<sub>5</sub> observed by ultrafast electron diffraction.

Nature communications·2026

Same journal

Demonstration of a quantum C-NOT gate in a time-multiplexed fully reconfigurable photonic processor.

Nature communications·2026

Same journal

Nonlinear quantum light source with van der Waals ferroelectric NbOX<sub>2</sub> (X = Br, I).

Nature communications·2026

Same journal

Antagonistic histone H2A variants and autonomous heterochromatin formation shape epigenomic patterns in Arabidopsis.

Nature communications·2026

Same journal

The long tail of nitrate pollution in groundwater challenges governance of global water quality.

Nature communications·2026

Same journal

Select microbial metabolites promote tau aggregation in a murine tauopathy model.

Nature communications·2026

Same journal

Warming climate has lengthened global intense tropical cyclone seasons.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2025

Molecular Beam Mass Spectrometry With Tunable Vacuum Ultraviolet VUV Synchrotron Radiation

Molecular Beam Mass Spectrometry With Tunable Vacuum Ultraviolet VUV Synchrotron Radiation

Published on: October 30, 2012

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction.

Fuhao Ji¹, Auralee Edelen², Ryan Roussel²

¹SLAC National Accelerator Laboratory, Menlo Park, 94025, California, USA. fuhaoji@slac.stanford.edu.

Nature Communications

|June 3, 2024

Summary

This summary is machine-generated.

We developed a multi-objective Bayesian active learning method to optimize electron beam properties for ultrafast electron diffraction (MeV-UED). This algorithm significantly speeds up the tuning process, reducing the need for manual adjustments and extensive measurements.

More Related Videos

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy

Published on: July 29, 2021

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Related Experiment Videos

Last Updated: Jun 24, 2025

Molecular Beam Mass Spectrometry With Tunable Vacuum Ultraviolet VUV Synchrotron Radiation

Molecular Beam Mass Spectrometry With Tunable Vacuum Ultraviolet VUV Synchrotron Radiation

Published on: October 30, 2012

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy

Published on: July 29, 2021

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Area of Science:

* Physical Chemistry and Materials Science
* Ultrafast Dynamics and Structural Analysis

Background:

* Ultrafast electron diffraction (UED) using MeV energy beams offers powerful insights into dynamic processes in matter.
* Optimizing electron beam properties for diverse scientific applications is complex, time-consuming, and often requires manual tuning.
* Developing efficient, algorithm-based online tuning strategies is crucial for advancing UED experiments.

Purpose of the Study:

* To demonstrate the efficacy of multi-objective Bayesian active learning for accelerating online beam tuning.
* To improve the efficiency and reduce the time required for optimizing electron probe properties in MeV-UED.
* To provide a systematic method for exploring complex parameter spaces in accelerator systems.

Main Methods:

* Implementation of a multi-objective Bayesian optimization algorithm at the SLAC MeV-UED facility.
* Efficiently searching the high-dimensional, nonlinear parameter space of the accelerator system.
* Mapping Pareto Fronts to understand trade-offs between critical electron beam properties.

Main Results:

* Successfully demonstrated multi-objective Bayesian active learning for online beam tuning.
* Achieved a significantly reduced number of measurements compared to traditional methods like grid scans.
* Provided an unprecedented overview of the experimental system's global behavior.

Conclusions:

* Multi-objective Bayesian active learning offers a highly efficient approach to optimize experimental parameters.
* This methodology drastically reduces tuning time and measurement requirements for MeV-UED.
* The developed scheme is broadly applicable to other complex systems requiring simultaneous optimization of multiple objectives.