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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
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...
48

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Self-assembly networks of azobenzene with hydrogen and halogen bonds on Au(111).

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相关实验视频

Updated: Jun 25, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

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可扩展并行算法用于图表神经网络分子动力学模拟中的原子间潜力模拟.

Yutack Park1, Jaesun Kim1, Seungwoo Hwang1

  • 1Department of Materials Science and Engineering and Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Korea.

Journal of chemical theory and computation
|May 30, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了SevenNet,这是一个用于图形神经网络原子间潜力 (GNN-IPs) 的可扩展包,可实现高效的大规模分子动力学模拟. 七网实现了高平行效率,桥梁机器学习和复杂的材料探索.

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Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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相关实验视频

Last Updated: Jun 25, 2025

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

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科学领域:

  • 计算材料科学科学 计算材料科学
  • 对于物理学的机器学习

背景情况:

  • 传递信息的图形神经网络原子间潜力 (GNN-IPs),像NequIP一样,提供数据效率和准确性.
  • 由于空间分解中的复杂数据通信,对分子动力学 (MD) 的GNN-IP平行化具有挑战性.

研究的目的:

  • 为GNN-IPs提出一个高效的并行化方案.
  • 开发一个可扩展的包,七网,用于大规模的MD模拟.
  • 为了使复杂的材料系统的准确和高效的探索.

主要方法:

  • 为GNN-IPs开发了基于NequIP架构的七网软件包.
  • 集成的七网与LAMMPS MD包.
  • 在32GPU集群上使用SiO2和无形Si3N4系统进行了基准测试.

主要成果:

  • 七网在弱扩展场景中实现了超过80%的并行效率.
  • 展示了几乎理想的强度缩放性能与完全的GPU利用.
  • 观察到的性能下降与低于最佳的GPU利用,特别是在小型系统或轻量级的模型.

结论:

  • 七网为大型MD模拟中GNN-IP提供了一个可扩展的解决方案.
  • 该套件有助于在材料研究中应用先进的机器学习模型.
  • 七网使研究人员能够以高精度和高效率研究复杂材料.