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

Intermolecular vs Intramolecular Forces

Intermolecular vs Intramolecular Forces

Intermolecular forces (IMF) are electrostatic attractions arising from charge-charge interactions between molecules. The strength of the intermolecular force is influenced by the distance of separation between molecules. The forces significantly affect the interactions in solids and liquids, where the molecules are close together. In gases, IMFs become important only under high-pressure conditions (due to the proximity of gas molecules). Intermolecular forces dictate the physical properties of...

Hydrogen Bonds

Hydrogen Bonds

A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...

Metal-Ligand Bonds

Metal-Ligand Bonds

The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...

Hybridization of Atomic Orbitals II

Hybridization of Atomic Orbitals II

sp3d and sp3d 2 Hybridization

Hybridization of Atomic Orbitals I

Hybridization of Atomic Orbitals I

The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...

You might also read

Related Articles

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

Sort by

Same author

Author Correction: Electrostatic potentials of atomic nanostructures at metal surfaces quantified by scanning quantum dot microscopy.

Nature communications·2026

Same author

Machine learning for smell: ordinal odor strength prediction of molecular perfumery components.

RSC advances·2026

Same author

DSKO: Dancing through DFTB Parametrization.

Journal of chemical theory and computation·2026

Same author

Mode selectivity in electron-promoted vibrational relaxation of chemisorbed hydrogen on molybdenum and tungsten surfaces.

Faraday discussions·2026

Same author

Revisiting the Maximum Hardness Principle: A Quantitative Analysis on Reaction Datasets.

Journal of computational chemistry·2026

Same author

Automated Discovery of Algorithms for Molecular Electronic Structure Calculations Using Physics-Informed Program Synthesis.

Journal of the American Chemical Society·2026

Search research articles

Related Experiment Video

Updated: Jul 7, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative

Wojciech G Stark¹, Julia Westermayr¹, Oscar A Douglas-Gallardo¹

¹Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.

The Journal of Physical Chemistry. C, Nanomaterials and Interfaces

|December 27, 2023

Summary

This summary is machine-generated.

This study uses ensemble learning and uncertainty quantification to improve machine learning models for predicting hydrogen reactions on copper surfaces, revealing limitations in current models for surface dynamics.

More Related Videos

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis

Published on: March 29, 2016

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

Published on: September 28, 2016

Related Experiment Videos

Last Updated: Jul 7, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis

Published on: March 29, 2016

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

Published on: September 28, 2016

Area of Science:

Surface science
Computational chemistry
Materials science

Background:

Molecular hydrogen's surface reactions are vital for energy storage and fuel cells.
Accurate theoretical prediction of these reactions is computationally demanding.
Machine learning potentials offer promise but require robust data generation and uncertainty assessment.

Purpose of the Study:

To develop and apply an ensemble learning approach with uncertainty quantification for gas-surface dynamics.
To investigate the performance of SchNet and PaiNN models for hydrogen scattering on copper.
To assess the impact of model uncertainty on predicting reaction probabilities.

Main Methods:

Adaptive training data generation using ensemble learning.
Full uncertainty quantification (UQ) for reaction probabilities.
Application and comparison of SchNet and PaiNN message-passing neural networks.

Main Results:

Ensemble-based UQ identified limitations in SchNet's invariant feature representation for gas-surface dynamics.
Iterative refinement of training data improved model reliability.
The study provides a framework for robust machine learning in surface reaction dynamics.

Conclusions:

Ensemble learning with UQ is crucial for reliable machine learning in surface chemistry.
PaiNN shows better performance than SchNet for this specific gas-surface dynamics problem.
Further development is needed for feature representations in machine learning potentials for surface reactions.