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Massively parallelization strategy for material simulation using high-dimensional neural network potential.

Cheng Shang1, Si-Da Huang1, Zhi-Pan Liu1

  • 1Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science (Ministry of Education), Department of Chemistry, Fudan University, Shanghai, 200433, China.

Journal of Computational Chemistry
|November 11, 2018
PubMed
Summary
This summary is machine-generated.

We developed a massive parallelization strategy for computing structural descriptors, accelerating potential energy surface (PES) calculations in material simulations using high-dimensional neural networks (HDNNs). This method significantly enhances computational efficiency.

Keywords:
neural networkparallelizationstructure descriptor

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Area of Science:

  • Computational Materials Science
  • Materials Simulation
  • Machine Learning in Materials

Background:

  • Potential energy surface (PES) calculations are computationally intensive and limit modern material simulations.
  • High-dimensional neural networks (HDNNs) offer a promising approach for fast and accurate PES computations.
  • The calculation of structural descriptors, crucial for HDNNs, represents a significant computational bottleneck.

Purpose of the Study:

  • To introduce a massive parallelization strategy for optimizing the computation of power-type structural descriptors.
  • To address the computational cost associated with HDNN-based PES calculations.
  • To enhance the efficiency of material simulations by accelerating descriptor computation.

Main Methods:

  • Developed a three-level massive parallelization strategy for structural descriptor computation.
  • Parallelization is applied sequentially over atoms, structural descriptors, and n-body functions.
  • The strategy is implemented and tested using a boron crystal system.

Main Results:

  • Demonstrated significant parallelization efficiency, reaching up to 100% at the atom level.
  • Achieved 58% and 34% parallelization efficiency at the structural descriptor and n-body function levels, respectively.
  • The proposed method effectively reduces the computational bottleneck in HDNN calculations.

Conclusions:

  • The introduced massive parallelization strategy substantially accelerates the computation of structural descriptors for HDNNs.
  • This optimization is critical for enabling faster and more accurate potential energy surface calculations in materials science.
  • The method provides a scalable solution for computationally demanding material simulations.