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

Random Sampling Method01:09

Random Sampling Method

15.1K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.1K
Simpson's Rule II01:28

Simpson's Rule II

98
In warehouse roofing applications, corrugated or curved metal sheets are commonly used to improve structural strength, water drainage, and ventilation efficiency. To accurately estimate material requirements and optimize design parameters, engineers must determine the curved surface area of these sheets. Because the sheet profiles often repeat smoothly along their length, they can be effectively approximated by parabolic curves, enabling the use of numerical integration techniques for area...
98
Cluster Sampling Method01:20

Cluster Sampling Method

15.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.0K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

3.0K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
3.0K

You might also read

Related Articles

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

Sort by
Same author

Machine Learning-Based Identification of Petroleum Distillates and Gasoline Traces Using Measured and Synthetic GC Spectra from Collected Samples.

Molecular informatics·2025
Same author

Dynamics and characteristics of misinformation related to earthquake predictions on Twitter.

Scientific reports·2023
Same author

A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations.

Molecular informatics·2023
Same author

Toward Developing Techniques─Agnostic Machine Learning Classification Models for Forensically Relevant Glass Fragments.

Journal of chemical information and modeling·2022
Same author

COVID-19 Conspiracy Theories Discussion on Twitter.

Social media + society·2022
Same author

Inter-laboratory workflow for forensic applications: Classification of car glass fragments.

Forensic science international·2022
Same journal

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

Journal of cheminformatics·2026
Same journal

Count your bits: fingerprint benchmarking to assess broad chemical space representation.

Journal of cheminformatics·2026
Same journal

Sampling out-of-distribution chemical spaces via Bayesian flow.

Journal of cheminformatics·2026
Same journal

Hold on tight: the kinetic profiling of opioid receptor ligands using the CORAL-MD.

Journal of cheminformatics·2026
Same journal

Transformer-accelerated discovery of inhibitors targeting the RpsA<sub>Δ438</sub> deletion in PZA-resistant tuberculosis.

Journal of cheminformatics·2026
Same journal

DICL: a manually curated database of ion channels and ligands as a useful platform for drug discovery targeting ion channels.

Journal of cheminformatics·2026
See all related articles

Related Experiment Video

Updated: Feb 19, 2026

In situ Grazing Incidence Small Angle X-ray Scattering on Roll-To-Roll Coating of Organic Solar Cells with Laboratory X-ray Instrumentation
06:49

In situ Grazing Incidence Small Angle X-ray Scattering on Roll-To-Roll Coating of Organic Solar Cells with Laboratory X-ray Instrumentation

Published on: March 2, 2021

6.8K

RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells.

Omer Kaspi1,2, Abraham Yosipof3, Hanoch Senderowitz4

  • 1Department of Systems Engineering, Afeka - Tel-Aviv Academic College of Engineering, Tel-Aviv, Israel.

Journal of Cheminformatics
|November 1, 2017
PubMed
Summary
This summary is machine-generated.

The RANdom SAmple Consensus (RANSAC) algorithm, new to material informatics, effectively builds Quantitative Structure Activity Relationship (QSAR) models for solar cells. It identifies key material descriptors and predicts photovoltaic properties for novel solar cell designs.

Keywords:
Material-informaticsPhotovoltaicsQSARRANSACSolar Cells

More Related Videos

Using Neutron Spin Echo Resolved Grazing Incidence Scattering to Investigate Organic Solar Cell Materials
06:05

Using Neutron Spin Echo Resolved Grazing Incidence Scattering to Investigate Organic Solar Cell Materials

Published on: January 15, 2014

8.3K
Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
14:01

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

Published on: May 22, 2015

43.4K

Related Experiment Videos

Last Updated: Feb 19, 2026

In situ Grazing Incidence Small Angle X-ray Scattering on Roll-To-Roll Coating of Organic Solar Cells with Laboratory X-ray Instrumentation
06:49

In situ Grazing Incidence Small Angle X-ray Scattering on Roll-To-Roll Coating of Organic Solar Cells with Laboratory X-ray Instrumentation

Published on: March 2, 2021

6.8K
Using Neutron Spin Echo Resolved Grazing Incidence Scattering to Investigate Organic Solar Cell Materials
06:05

Using Neutron Spin Echo Resolved Grazing Incidence Scattering to Investigate Organic Solar Cell Materials

Published on: January 15, 2014

8.3K
Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
14:01

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

Published on: May 22, 2015

43.4K

Area of Science:

  • Material Informatics
  • Chemoinformatics
  • Computational Materials Science

Background:

  • Quantitative Structure Activity Relationship (QSAR) models are crucial in chemoinformatics and material informatics.
  • The RANdom SAmple Consensus (RANSAC) algorithm is a robust tool for noise reduction in datasets, commonly used in image processing.
  • RANSAC's potential as an integrated solution for QSAR model development, validation, and prediction, including applicability domain assessment, is recognized.

Purpose of the Study:

  • To introduce and evaluate the RANSAC algorithm for the first time in material informatics for solar cell analysis.
  • To demonstrate RANSAC's capability in developing and validating QSAR models for metal oxide (MO) based solar cells.
  • To explore the predictive power of RANSAC for photovoltaic properties and identify structure-property relationships in novel solar cell materials.

Main Methods:

  • Application of the RANSAC algorithm for QSAR model development and validation.
  • Utilizing RANSAC for outlier removal and descriptor selection from solar cell datasets.
  • Employing RANSAC to predict photovoltaic properties and assess the applicability domain for new material compositions.

Main Results:

  • RANSAC successfully identified descriptors known to correlate with photovoltaic properties in three different metal oxide solar cell datasets.
  • Models derived using RANSAC demonstrated good predictive statistics for key photovoltaic properties.
  • The study successfully predicted properties of virtual solar cell libraries, revealing dependencies on MO composition.

Conclusions:

  • RANSAC is a viable and effective "one stop shop" algorithm for QSAR modeling in material informatics, specifically for solar cells.
  • RANSAC facilitates the discovery of structure-property relationships, aiding in the design of efficient solar cell materials.
  • The application of RANSAC opens new avenues for accelerating materials discovery and optimization in the field of photovoltaics.