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

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

838
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
838
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

6.9K
At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
6.9K
Thermal and Photochemical Electrocyclic Reactions: Overview01:26

Thermal and Photochemical Electrocyclic Reactions: Overview

2.6K
Electrocyclic reactions are reversible reactions. They involve an intramolecular cyclization or ring-opening of a conjugated polyene. Shown below are two examples of electrocyclic reactions. In the first reaction, the formation of the cyclic product is favored. In contrast, in the second reaction, ring-opening is favored due to the high ring strain associated with cyclobutene formation.
2.6K

You might also read

Related Articles

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

Sort by
Same author

High-Accuracy Machine Learning Projections of Composition-Dependent Thermal Stability in Halide Perovskites.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Reversible, Photo-Induced Lattice Distortions in Halide Perovskites.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

An AI-accelerated pathway for reproducible and stable halide perovskites.

Chemical Society reviews·2025
Same author

Transition-Metal Nitrides for High-Temperature Structural Colors.

ACS applied materials & interfaces·2025
Same author

In Situ Solid-State Dewetting of Ag-Au-Pd Alloy: From Macro- to Nanoscale.

ACS applied materials & interfaces·2024
Same author

Broadband Superabsorber Operating at 1500 °C Using Dielectric Bilayers.

ACS applied optical materials·2023

Related Experiment Video

Updated: Oct 24, 2025

Flash Infrared Annealing for Perovskite Solar Cell Processing
05:15

Flash Infrared Annealing for Perovskite Solar Cell Processing

Published on: February 3, 2021

8.2K

Machine Learning Roadmap for Perovskite Photovoltaics.

Meghna Srivastava1, John M Howard2, Tao Gong1,3

  • 1Department of Materials Science and Engineering, University of California, Davis, California 95616, United States.

The Journal of Physical Chemistry Letters
|August 12, 2021
PubMed
Summary

Machine learning and autonomous experimentation accelerate perovskite solar cell (PSC) development. These advanced tools offer a roadmap for faster material discovery and stability testing, paving the way for commercialization.

More Related Videos

Influence of Hybrid Perovskite Fabrication Methods on Film Formation, Electronic Structure, and Solar Cell Performance
11:38

Influence of Hybrid Perovskite Fabrication Methods on Film Formation, Electronic Structure, and Solar Cell Performance

Published on: February 27, 2017

18.7K
Monovalent Cation Doping of CH3NH3PbI3 for Efficient Perovskite Solar Cells
08:30

Monovalent Cation Doping of CH3NH3PbI3 for Efficient Perovskite Solar Cells

Published on: March 19, 2017

16.8K

Related Experiment Videos

Last Updated: Oct 24, 2025

Flash Infrared Annealing for Perovskite Solar Cell Processing
05:15

Flash Infrared Annealing for Perovskite Solar Cell Processing

Published on: February 3, 2021

8.2K
Influence of Hybrid Perovskite Fabrication Methods on Film Formation, Electronic Structure, and Solar Cell Performance
11:38

Influence of Hybrid Perovskite Fabrication Methods on Film Formation, Electronic Structure, and Solar Cell Performance

Published on: February 27, 2017

18.7K
Monovalent Cation Doping of CH3NH3PbI3 for Efficient Perovskite Solar Cells
08:30

Monovalent Cation Doping of CH3NH3PbI3 for Efficient Perovskite Solar Cells

Published on: March 19, 2017

16.8K

Area of Science:

  • Materials Science
  • Renewable Energy
  • Artificial Intelligence

Background:

  • Perovskite solar cells (PSCs) show promise for next-generation solar energy, matching silicon photovoltaic efficiencies.
  • Long-term stability remains a critical barrier to the commercialization of PSCs.
  • Traditional methods for PSC development and testing are time-consuming and labor-intensive.

Purpose of the Study:

  • To survey the application of machine learning (ML) and autonomous experimentation in perovskite solar cell research.
  • To propose a roadmap for integrating ML across all stages of PSC design and development.
  • To highlight challenges and future directions for accelerating PSC advancement.

Main Methods:

  • Review of machine learning techniques applicable to materials science problems.
  • Discussion of autonomous experimentation platforms for accelerated testing.
  • Proposal of an integrated ML-driven pipeline for PSC research.

Main Results:

  • ML and autonomous experimentation offer powerful toolkits for gaining physical understanding.
  • These methods can significantly accelerate the screening, synthesis, and testing of perovskite materials and devices.
  • A comprehensive roadmap for ML application in PSC research is presented.

Conclusions:

  • Machine learning and autonomous experimentation are crucial for overcoming the limitations of traditional PSC development.
  • An integrated pipeline leveraging these technologies can expedite the path to commercialization for perovskite solar cells.
  • Further research is needed to address challenges and fully realize the potential of ML in this field.