Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Trajectory models of serum creatinine and 28-day mortality in critically ill patients with sepsis complicated by type 2 diabetes mellitus: a cohort study.

Frontiers in endocrinology·2026
Same author

Effects of opioid-free anesthesia on postoperative anxiety in patients undergoing modified radical mastectomy for breast cancer: a randomized controlled trial.

BMC anesthesiology·2026
Same author

SIRT4 Alleviates Retinal Ischemia-Reperfusion Injury Via Mediating Astrocytes Lipid Metabolism and Mitochondrial Function.

Investigative ophthalmology & visual science·2026
Same author

Genetically predicted N-acetylisoleucine levels mediate the association between PB/PC% lymphocyte and normal pressure hydrocephalus: A mediation Mendelian randomization study.

Medicine·2026
Same author

Automated camouflage pattern design based on conditional generative adversarial network and image quilting.

Scientific reports·2026
Same author

Perioperative esketamine for prevention of postoperative sleep disturbance after anesthesia: a systematic review and meta-analysis of randomized controlled trials.

Frontiers in pharmacology·2026

Related Experiment Video

Updated: Jun 21, 2025

Atomic Layer Deposition of Vanadium Dioxide and a Temperature-dependent Optical Model
11:10

Atomic Layer Deposition of Vanadium Dioxide and a Temperature-dependent Optical Model

Published on: May 23, 2018

11.9K

Preparation of Thermochromic Vanadium Dioxide Films Assisted by Machine Learning.

Gaoyang Xiong1, Haining Ji1, Yongxing Chen1

  • 1School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China.

Nanomaterials (Basel, Switzerland)
|July 13, 2024
PubMed
Summary

Machine learning accelerates the development of vanadium dioxide (VO2) smart window films. By optimizing preparation parameters, researchers achieved pure-phase VO2(M) films, reducing experimental waste and costs.

Keywords:
VO2(M)energy-saving materialextreme gradient boostingmachine learningmagnetron sputtering

More Related Videos

Chemical Vapor Deposition of an Organic Magnet, Vanadium Tetracyanoethylene
08:25

Chemical Vapor Deposition of an Organic Magnet, Vanadium Tetracyanoethylene

Published on: July 3, 2015

11.5K
Preparation of Polyoxometalate-based Photo-responsive Membranes for the Photo-activation of Manganese Oxide Catalysts
05:47

Preparation of Polyoxometalate-based Photo-responsive Membranes for the Photo-activation of Manganese Oxide Catalysts

Published on: August 7, 2018

7.7K

Related Experiment Videos

Last Updated: Jun 21, 2025

Atomic Layer Deposition of Vanadium Dioxide and a Temperature-dependent Optical Model
11:10

Atomic Layer Deposition of Vanadium Dioxide and a Temperature-dependent Optical Model

Published on: May 23, 2018

11.9K
Chemical Vapor Deposition of an Organic Magnet, Vanadium Tetracyanoethylene
08:25

Chemical Vapor Deposition of an Organic Magnet, Vanadium Tetracyanoethylene

Published on: July 3, 2015

11.5K
Preparation of Polyoxometalate-based Photo-responsive Membranes for the Photo-activation of Manganese Oxide Catalysts
05:47

Preparation of Polyoxometalate-based Photo-responsive Membranes for the Photo-activation of Manganese Oxide Catalysts

Published on: August 7, 2018

7.7K

Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Materials Science

Background:

  • Smart windows adjust solar radiation using materials like Vanadium Dioxide (VO2).
  • VO2(M) exhibits a phase transition at 68°C, ideal for energy-saving applications.
  • Challenges persist in preparing pure-phase VO2(M) due to vanadium's complex chemistry.

Purpose of the Study:

  • To explore machine learning (ML) algorithms for optimizing VO2(M) film preparation.
  • To identify key parameters influencing the successful synthesis of pure-phase VO2(M).
  • To demonstrate the feasibility of ML in reducing material preparation costs and time.

Main Methods:

  • Investigated four ML algorithms: MLP, RF, SVM, and XGBoost.
  • Utilized magnetron sputtering for VO2(M) film deposition.
  • Employed SHAP analysis for feature importance assessment.

Main Results:

  • Extreme Gradient Boosting (XGBoost) achieved the highest prediction accuracy (88.52%).
  • Substrate temperature was identified as a critical parameter for VO2(M) preparation.
  • Optimized parameters led to the successful experimental synthesis of pure-phase VO2(M) films.

Conclusions:

  • ML-assisted material preparation is highly effective for VO2(M) films.
  • This approach significantly reduces experimental trial-and-error, saving resources.
  • Facilitates faster optimization of material synthesis for smart window applications.