Jove
Visualize
Contact Us

Related Concept Videos

Quantum Numbers02:43

Quantum Numbers

42.9K
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.
42.9K
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

50.7K
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.
50.7K
Distillation: Vapor–Liquid Equilibria01:01

Distillation: Vapor–Liquid Equilibria

3.0K
Distillation is a separation technique that takes advantage of the boiling point properties of disparate elements in a mixture. To perform distillation, we begin by heating a miscible mixture of two liquids with a significant difference in boiling points (at least 20°C). As the solution heats up and reaches the bubble point of the more volatile component, some molecules of the more volatile component transition into the gas phase and travel upward into the condenser, which is a glass tube...
3.0K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.3K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.3K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.8K
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
1.8K
Thermodynamic Potentials01:26

Thermodynamic Potentials

1.0K
Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
1.0K

You might also read

Related Articles

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

Sort by
Same journal

The influence of chirality on the macroscopic behavior of multiferroic smectic phases.

The Journal of chemical physics·2026
Same journal

Polaron transformed canonically consistent quantum master equation.

The Journal of chemical physics·2026
Same journal

The x-ray absorption spectrum of the propargyl radical C3H3●.

The Journal of chemical physics·2026
Same journal

Transient hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation. I. Conformer- and isomer-resolved infrared spectra.

The Journal of chemical physics·2026
Same journal

Transient hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation. II. Isomer-resolved unimolecular dynamics.

The Journal of chemical physics·2026
Same journal

Quantum state-to-state dynamics studies of the C(3P) + OH(X2Π) → CO(a3Π) + H(2S) reaction based on a new HCO(12A″) potential energy surface.

The Journal of chemical physics·2026
See all related articles
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: Oct 1, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.6K

DQC: A Python program package for differentiable quantum chemistry.

Muhammad F Kasim1, Susi Lehtola2, Sam M Vinko1

  • 1Department of Physics, Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom.

The Journal of Chemical Physics
|March 2, 2022
PubMed
Summary
This summary is machine-generated.

Automatic differentiation simplifies scientific programming by enabling gradient calculations, accelerating machine learning and quantum chemistry simulations. This study introduces an open-source code for differentiable quantum chemistry, showcasing its utility in various molecular property predictions.

More Related Videos

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

675
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.3K

Related Experiment Videos

Last Updated: Oct 1, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.6K
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

675
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.3K

Area of Science:

  • Computational chemistry
  • Scientific programming
  • Machine learning

Background:

  • Automatic differentiation (AD) is revolutionizing scientific programming by automating derivative calculations.
  • AD simplifies gradient computation, reducing development time and calculation complexity.
  • AD has driven advances in machine learning and is increasingly applied to quantum systems.

Purpose of the Study:

  • To present an open-source differentiable quantum chemistry simulation code.
  • To explore applications of AD in computational quantum chemistry.
  • To demonstrate the utility of AD for molecular property prediction and simulation.

Main Methods:

  • Development of an open-source differentiable quantum chemistry simulation package.
  • Application of AD to calculate molecular perturbation properties.
  • Utilizing AD for basis set reoptimization and wave function stability analysis.
  • Employing AD for alchemical perturbation predictions of molecular properties.

Main Results:

  • The developed code enables efficient gradient calculations for quantum chemistry simulations.
  • Demonstrated applications include molecular perturbation calculations and basis set optimization.
  • The code facilitates stability checks of self-consistent field wave functions.
  • Accurate prediction of molecular properties through alchemical perturbations is achieved.

Conclusions:

  • Automatic differentiation offers a powerful paradigm shift for computational quantum chemistry.
  • The open-source differentiable code expands the capabilities of quantum simulations.
  • AD enhances the prediction and analysis of molecular properties, accelerating research.