MD simulation of hundred-billion-metal-atom cascade collision on Sunway Taihulight

Chu, Genshen; Li, Yang; Zhao, Runchu; Ren, Shuai; Yang, Wen; He, Xinfu; Hu, Changjun; Wang, Jue

doi:10.1016/j.cpc.2021.108128

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Cited by 8 publications

(2 citation statements)

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“…The current atomistic molecular dynamics ''world record'' for the largest simulation 41 corresponds to a molecular dynamics (MD) simulation of 3.02. 10 13 atoms.…”

Section: Hardwarementioning

confidence: 99%

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

Tillotson

Diamantonis

Buda³

et al. 2023

Phys. Chem. Chem. Phys.

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“…The current atomistic molecular dynamics ''world record'' for the largest simulation 41 corresponds to a molecular dynamics (MD) simulation of 3.02. 10 13 atoms.…”

Section: Hardwarementioning

confidence: 99%

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

Tillotson

Diamantonis

Buda³

et al. 2023

Phys. Chem. Chem. Phys.

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“…The cascade defects data was obtained by MISA-MD, the fastest molecular dynamic simulation software for simulating radiation cascade in reactor pressure vessels developed by USTB [13], and the open source address is https://hpcde.github.io/p/open source/. The atoms are all of Fe with a BCC crystal structure and the lattice constant is 2.85532.…”

Section: Data Preparationmentioning

confidence: 99%

Interpretable Feature Extraction for the Numerical Particle System

Ren,

Zhang,

et al.

Proceedings of the Eleventh International Conference on Engineering Computational Technology

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Particle system analysis is important but costly in solving many physical problems since particles are the basic composition of almost everything, and machine learning is increasingly used in optimization for numerical simulations. The most important and difficult job is to make the particle system understandable by the machine learning model, which usually called feature extraction. In this paper, a novel method and an accurate physical interpretation for feature extraction of cascade defects data, an important particle system in reactor pressure vessel analysis, was proposed. Four strategies were designed to extract features from cascade defects data in which the DAPP shows the best performance. This study shows that feature extraction based on physical information has a positive significance for the analysis of particle systems and provide a theoretical support for improving the physical interpretability of machine learning models on particle systems.

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Atomistic modeling of pulsed laser ablation in liquid: spatially and time-resolved maps of transient nonequilibrium states and channels of nanoparticle formation

Chen

Zhigilei

2023

Appl. Phys. A

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MD simulation of hundred-billion-metal-atom cascade collision on Sunway Taihulight

Cited by 8 publications

References 38 publications

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

Interpretable Feature Extraction for the Numerical Particle System

Atomistic modeling of pulsed laser ablation in liquid: spatially and time-resolved maps of transient nonequilibrium states and channels of nanoparticle formation

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