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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...
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Related Experiment Video

Updated: Dec 16, 2025

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
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Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

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Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch.

Guoqing Zhou1, Ben Nebgen2, Nicholas Lubbers2

  • 1Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, United States.

Journal of Chemical Theory and Computation
|July 2, 2020
PubMed
Summary
This summary is machine-generated.

PYSEQM is a new open-source software for fast and accurate molecular dynamics simulations using PyTorch. It enables efficient quantum mechanical calculations on GPUs, supporting machine learning for model parameterization.

Related Experiment Videos

Last Updated: Dec 16, 2025

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

505

Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Molecular Dynamics

Background:

  • Born-Oppenheimer molecular dynamics (BOMD) is crucial for simulating molecular behavior.
  • Existing BOMD implementations can be computationally intensive and lack GPU acceleration.
  • Semiempirical quantum mechanics (SEQM) offers a balance between accuracy and computational cost.

Purpose of the Study:

  • To introduce PYSEQM, an open-source, high-performance implementation of BOMD.
  • To leverage PyTorch for efficient GPU computation and automatic differentiation.
  • To provide a scalable and stable engine for quantum-based molecular dynamics.

Main Methods:

  • Developed PYSEQM using PyTorch for GPU acceleration and automatic differentiation.
  • Implemented SEQM methods including MNDO, AM1, and PM3.
  • Incorporated advanced algorithms like recursive Fermi-operator expansion (SP2) and extended Lagrangian BOMD.

Main Results:

  • PYSEQM demonstrates high performance on GPU hardware.
  • Automatic differentiation facilitates machine learning-based model parameterization.
  • Benchmark tests show efficient scaling, stability in energy conservation, and fast, accurate computations.

Conclusions:

  • PYSEQM offers a powerful, open-source tool for computational chemists.
  • The software enables rapid and accurate quantum-based molecular dynamics simulations.
  • PYSEQM supports advanced features like ML-driven parameterization and GPU acceleration.