Jove
Visualize
Contact Us

Related Concept Videos

2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

285
Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
285
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

311
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
311
2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

275
Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
COSY90 is the standard two-dimensional (2D) COSY experiment that...
275
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

33.2K
sp3d and sp3d 2 Hybridization
33.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

686
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
686
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

48.2K
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...
48.2K

You might also read

Related Articles

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

Sort by
Same author

On the practical applicability of DM21 neural-network DFT functional for chemical calculations: Focus on geometry optimization.

The Journal of chemical physics·2025
Same author

Modelling of macrophage responses to biomaterials <i>in vitro</i>: state-of-the-art and the need for the improvement.

Frontiers in immunology·2024
Same author

Ultrafast Polarization Switching in BaTiO<sub>3</sub> Nanomaterials: Combined Density Functional Theory and Coupled Oscillator Study.

ACS omega·2024
Same author

Solid-liquid phase transition inside van der Waals nanobubbles: an atomistic perspective.

Physical chemistry chemical physics : PCCP·2023
Same author

Process Parameter Selection for Production of Stainless Steel 316L Using Efficient Multi-Objective Bayesian Optimization Algorithm.

Materials (Basel, Switzerland)·2023
Same author

nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset.

Physical chemistry chemical physics : PCCP·2022
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 Experiment Video

Updated: Aug 31, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.3K

Application of two-component neural network for exchange-correlation functional interpolation.

Alexander Ryabov1,2, Iskander Akhatov3, Petr Zhilyaev3

  • 1Center for Materials Technologies, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, Moscow, 121205, Russia. ununbium17@gmail.com.

Scientific Reports
|August 19, 2022
PubMed
Summary

Neural networks (NN) offer a unified approach to developing accurate exchange-correlation (XC) functionals for Density Functional Theory (DFT). This new NN XC functional provides reliable results for atoms, molecules, and crystals.

More Related Videos

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.3K
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.6K

Related Experiment Videos

Last Updated: Aug 31, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.3K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.3K
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.6K

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Quantum Mechanics

Background:

  • Density Functional Theory (DFT) is crucial for solving the many-body Schrodinger equation.
  • The accuracy of DFT relies heavily on approximations for the exchange-correlation (XC) functional, which lacks an analytical form.
  • Current XC functional development often uses heuristic rules and limited data sources.

Purpose of the Study:

  • To develop a novel, unified approach for parametrizing XC functionals using neural networks (NN).
  • To improve the accuracy and flexibility of XC functionals by leveraging data from high-precision theories and quantum chemical databases.
  • To create an NN XC functional that directly provides exchange potential and energy density without requiring derivatives.

Main Methods:

  • Development of a two-part neural network architecture (NN-E and NN-V) for XC functional parametrization.
  • Training the NN components separately to enhance flexibility.
  • Utilizing data from higher-precision theories and quantum chemical databases for NN training.
  • Testing the NN XC functional for convergence in self-consistent cycles and accuracy on atoms, molecules, and crystals.

Main Results:

  • The proposed NN architecture successfully interpolates data from higher-precision theories.
  • The NN XC functional provides exchange potential and energy density without direct derivatives.
  • The developed functional demonstrates convergence in self-consistent field calculations.
  • Reasonable energies were obtained when applied to various atomic, molecular, and crystalline systems.

Conclusions:

  • Neural network-based parametrization offers a promising and unified path for developing accurate XC functionals in DFT.
  • The flexible, two-part NN architecture allows for improved accuracy and adaptability.
  • The NN XC functional shows potential for widespread application in computational chemistry and materials science.