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

Properties of Transition Metals

Properties of Transition Metals

Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy

Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy

The kinetic molecular theory qualitatively explains the behaviors described by the various gas laws. The postulates of this theory may be applied in a more quantitative fashion to derive these individual laws.

Energy Diagrams, Transition States, and Intermediates

Energy Diagrams, Transition States, and Intermediates

Free-energy diagrams, or reaction coordinate diagrams, are graphs showing the energy changes that occur during a chemical reaction. The reaction coordinate represented on the horizontal axis shows how far the reaction has progressed structurally. Positions along the x-axis close to the reactants have structures resembling the reactants, while positions close to the products resemble the products. Peaks on the energy diagram represent stable structures with measurable lifetimes, while...

Reliability and Validity

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

Molecular Kinetic Energy

Molecular Kinetic Energy

The word "gas" comes from the Flemish word meaning "chaos," first used to describe vapors by the chemist J. B. van Helmont. Consider a container filled with gas, with a continuous and random motion of molecules. During collisions, the velocity component parallel to the wall is unchanged, and the component perpendicular to the wall reverses direction but does not change in magnitude. If the molecule’s velocity changes in the x-direction, then its momentum is changed.

You might also read

Related Articles

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

Sort by

Same author

Contrast Agents for Enhanced Bioimaging: A Comprehensive Review.

Chembiochem : a European journal of chemical biology·2026

Same author

Gas-Phase Formation of Silicon Dicarbide (c-SiC<sub>2</sub>): A Key Cyclic Precursor to Silicon Carbide Grains in Space.

The journal of physical chemistry letters·2026

Same author

Coverage Effects on Hydrogen Evolution across Metals, Oxides, MXenes, and Dichalcogenides.

ACS omega·2026

Same author

Combining DFT Calculations and Clustering Techniques to Screen Organic Monovalent Cations for Applications in Halide Perovskite Solar Cells.

ACS omega·2026

Same author

Electrochemical Nitrate and Nitrite Reduction Reaction to Ammonia: Catalytic Aging and Stability of Co<sub>3</sub>O<sub>4</sub> Hexagonal Nanoplates.

ACS applied materials & interfaces·2026

Same author

Enhancing Surface Termination and Stability of Hybrid Halide Perovskites via Phosphonic Acid Passivation.

ACS omega·2026

Search research articles

Related Experiment Video

Updated: Jan 28, 2026

Experimental System of Solar Adsorption Refrigeration with Concentrated Collector

Experimental System of Solar Adsorption Refrigeration with Concentrated Collector

Published on: October 18, 2017

Optimizing Molecular Descriptors for Reliable Adsorption Energy Prediction on Transition Metal Nanoclusters.

Lucas B Pena¹, Felipe V Calderan², Priscilla Felício-Sousa³

¹Centro Federal de Educação Tecnológica de Minas Gerais, 30421-169 Belo Horizonte, MG, Brazil.

|January 26, 2026

Summary

Machine learning models can predict catalytic activity using structural descriptors. Adding electronic features improved model generalizability for transition-metal nanoclusters, reducing costly experimental studies.

More Related Videos

Synthesis of Near-Infrared Emitting Gold Nanoclusters for Biological Applications

Synthesis of Near-Infrared Emitting Gold Nanoclusters for Biological Applications

Published on: March 22, 2020

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction

Published on: April 10, 2018

Related Experiment Videos

Last Updated: Jan 28, 2026

Experimental System of Solar Adsorption Refrigeration with Concentrated Collector

Experimental System of Solar Adsorption Refrigeration with Concentrated Collector

Published on: October 18, 2017

Synthesis of Near-Infrared Emitting Gold Nanoclusters for Biological Applications

Synthesis of Near-Infrared Emitting Gold Nanoclusters for Biological Applications

Published on: March 22, 2020

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction

Published on: April 10, 2018

Area of Science:

Catalysis
Materials Science
Computational Chemistry

Background:

Efficient catalytic processes are vital for transforming pollutants into valuable chemicals.
Transition-metal nanoclusters offer tunable properties for catalysis but require extensive studies for active site identification.
Density functional theory (DFT) calculations are used to determine adsorption energetics, but are computationally expensive.

Purpose of the Study:

To evaluate the predictive performance and transferability of structural descriptors for machine learning models in catalysis.
To assess the impact of different descriptors on predicting adsorption energies of adsorbates on transition-metal nanoclusters.
To explore methods for improving the generalizability of machine learning models in catalyst discovery.

Main Methods:

Utilized random forest regression algorithm to train machine learning models.
Employed Coulomb matrix and many-body tensor representation as structural descriptors.
Tested model performance on diverse nanocluster-adsorbate data sets, including external, unprecedented examples.
Incorporated electronic features, such as the number of unpaired electrons, to enhance model generalizability.

Main Results:

Both Coulomb matrix and many-body tensor representation achieved a mean absolute error of 0.05 eV on the test data.
Model performance significantly decreased when applied to an external dataset with novel examples.
Including the number of unpaired electrons as an electronic feature improved model generalizability, despite slightly higher mean absolute errors.

Conclusions:

Structural descriptors alone show limitations in generalizability for diverse catalytic systems.
Machine learning models for catalysis are highly dependent on the quality and diversity of training data.
Incorporating relevant electronic features alongside structural descriptors can enhance the predictive power and transferability of catalytic models.