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

Quantum Numbers02:43

Quantum Numbers

34.9K
It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
34.9K
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

42.5K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
42.5K
Machines: Problem Solving II01:30

Machines: Problem Solving II

335
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
335
Sampling Theorem01:15

Sampling Theorem

382
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
382
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.5K
VSEPR Theory for Determination of Electron Pair Geometries
34.5K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.1K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.1K

You might also read

Related Articles

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

Sort by
Same authorSame journal

Double Asymptotic Expansion of Three-Center Electron Repulsion Integrals 2nd Derivatives.

Journal of chemical theory and computation·2026
Same author

Organophosphate ethyl paraoxon on Ag and Au surfaces: a density functional theory perspective.

Physical chemistry chemical physics : PCCP·2026
Same author

Nuclear Spin-Spin Coupling Constants from Auxiliary Density Functional Theory.

Journal of chemical theory and computation·2026
Same author

Mode-resolved electron-phonon fingerprints map topological protection in lithium beyond born-Oppenheimer.

Journal of physics. Condensed matter : an Institute of Physics journal·2026
Same author

New range-separated screened and full-range hybrid functionals.

The Journal of chemical physics·2026
Same author

Exploring quantum active learning for materials design and discovery.

Physical chemistry chemical physics : PCCP·2026

Related Experiment Video

Updated: Jul 19, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

604

QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery.

Maicon Pierre Lourenço1, Lizandra Barrios Herrera2, Jiří Hostaš2

  • 1Departamento de Química e Física─Centro de Ciências Exatas, Naturais e da Saúde─CCENS─Universidade Federal do Espírito Santo, Alegre, Espírito Santo 29500-000, Brasil.

Journal of Chemical Theory and Computation
|August 15, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces QMLMaterial, an AI tool for accelerating materials discovery by predicting optimal structures using quantum machine learning. It efficiently explores vast chemical spaces for various systems, reducing computational costs.

More Related Videos

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

12.0K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.5K

Related Experiment Videos

Last Updated: Jul 19, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

604
Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

12.0K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.5K

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Artificial Intelligence

Background:

  • Experimental structural elucidation is complex.
  • Theoretical chemistry aids in understanding material properties but faces search space limitations.
  • Global search algorithms are crucial for identifying optimal structures.

Purpose of the Study:

  • To present QMLMaterial, an AI-powered software for automated in silico structural determination.
  • To enable efficient discovery of optimal structures across diverse chemical systems.
  • To reduce the computational cost of materials design and discovery.

Main Methods:

  • Utilizes an active learning approach with machine learning regression algorithms.
  • Employs uncertainty quantification (Bayesian statistics, K-fold cross-validation, bootstrap resampling) for informed structure selection.
  • Integrates with quantum chemistry codes and atomic descriptors (e.g., many-body tensor representation).

Main Results:

  • Demonstrates QMLMaterial's capability in determining structures for atomic clusters, doped systems, adsorbed molecules, and encapsulated clusters.
  • Successfully applied to systems like Na20, Mo6C3 (including spin multiplicity), H2O@CeNi3O5, Mg8@graphene, and Na3Mg3@CNT.
  • The active learning strategy enhances the probability of finding global minima with fewer calculations.

Conclusions:

  • QMLMaterial offers a powerful and efficient platform for accelerating materials design and discovery.
  • The AI-driven approach overcomes limitations of traditional computational methods.
  • Facilitates the exploration of complex chemical systems for novel material identification.