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Related Concept Videos

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Thermodynamic Potentials01:26

Thermodynamic Potentials

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...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...

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Related Experiment Videos

Empowering neural network-based quantum Monte Carlo with local pseudopotentials.

Weizhong Fu1,2, Ryunosuke Fujimaru3, Ruichen Li1

  • 1ByteDance Seed, Beijing, People's Republic of China.

Nature Computational Science
|July 10, 2026
PubMed
Summary

Neural network-based quantum Monte Carlo (NNQMC) now efficiently handles large quantum systems. A new framework using local pseudopotentials improves computational speed and accuracy for complex molecular calculations.

Related Experiment Videos

Area of Science:

  • Computational quantum chemistry
  • Materials science
  • Quantum many-body physics

Background:

  • Neural network-based quantum Monte Carlo (NNQMC) is a powerful method for high-accuracy solutions to quantum systems.
  • Current NNQMC applications are limited to small systems due to significant computational demands.

Purpose of the Study:

  • To enhance the computational efficiency and scalability of NNQMC.
  • To enable the accurate calculation of larger and more complex quantum systems.

Main Methods:

  • Introduction of a novel framework utilizing local pseudopotentials within NNQMC.
  • Reduction of the electron count treated by the neural network.
  • Avoidance of computationally expensive integration terms.

Main Results:

  • Achieved improved computational efficiency and scalability for NNQMC.
  • Demonstrated superior relative energy accuracy compared to all-electron NNQMC for complex systems.
  • Showcased the ability to reliably treat large systems, exemplified by the Fe4S4(SCH3)4 iron-sulfur cluster.
  • Outperformed widely used semilocal pseudopotential methods in terms of efficiency.

Conclusions:

  • The synergy between NNQMC and local pseudopotentials significantly expands the applicability of accurate ab initio calculations.
  • This approach overcomes previous limitations, paving the way for studying larger, more complex molecular systems.