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Related Concept Videos

Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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...
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
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...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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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Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
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Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.

Junfan Xia1,2, Bin Jiang1,2,3

  • 1State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China.

JACS Au
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

We developed an efficient parallelization scheme for message passing neural networks (MPNNs) to accelerate atomistic simulations. This method enables large-scale simulations and provides insights into graphene formation, optimizing material synthesis.

Keywords:
graphene formationmachine learning potentialmessage passing neural networkmolecular dynamicsparallel algorithm

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Area of Science:

  • Computational materials science
  • Artificial intelligence in chemistry
  • Atomistic simulations

Background:

  • Machine learning potentials, particularly message passing neural networks (MPNNs), accelerate atomistic simulations.
  • MPNNs offer high accuracy but face challenges in efficient parallelization due to their semilocal architecture.

Purpose of the Study:

  • To propose and validate an efficient parallelization scheme for MPNN potentials.
  • To enable large-scale atomistic simulations using MPNNs.
  • To investigate the role of oxygen in graphene formation via reactive molecular dynamics (MD).

Main Methods:

  • Developed a novel parallelization scheme for MPNN potentials minimizing data communication.
  • Tested the scheme on bulk systems (silver, liquid water, high-entropy alloys) for strong and weak scaling.
  • Created a universal MPNN potential for C, H, O, N elements.
  • Performed reactive MD simulations of graphene formation from acetylene-oxygen detonation.

Main Results:

  • Demonstrated excellent strong- and weak-scaling performance up to 100 million atoms.
  • Achieved linear scaling with message-passing depth, overcoming MPNN parallelization limitations.
  • Revealed oxygen's mediating role in the reaction network for graphene formation.
  • Identified optimal O2/C2H2 ratios for graphene-precursor formation.

Conclusions:

  • The proposed parallelization framework significantly extends the applicability of MPNN potentials to unprecedented scales.
  • The study provides mechanistic insights into oxygen's crucial role in graphene synthesis.
  • The parallelization scheme is adaptable for other MPNN potentials and large-scale simulations.