基于材料数值计算大数据的材料辐照机理发现

任帅; 陈丹丹; 储根深; 白鹤; 李慧昭; 何远杰; 胡长军

doi:10.11959/j.issn.2096-0271.2021056

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基于材料数值计算大数据的材料辐照机理发现

专题：大数据支撑的智能应用 | 更新时间：2024-06-03

- 基于材料数值计算大数据的材料辐照机理发现
- Discovery of irradiation mechanism based on big data of material simulation
- 大数据 2021年7卷第6期页码：3-18
- 作者机构：
  
  1. 北京科技大学计算机与通信工程学院，北京 100083
  2. 智能超算融合应用技术教育部工程研究中心，北京 100083
- 作者简介：
  
  [ "任帅（1992- ），男，北京科技大学计算机与通信工程学院博士生，主要研究方向为机器学习、大数据及数据挖掘等" ]
  [ "陈丹丹（1995- ），女，北京科技大学计算机与通信工程学院博士生，主要研究方向为软件工程、数值计算及数据挖掘等" ]
  [ "储根深（1994- ），男，北京科技大学计算机与通信工程学院博士生，主要研究方向为并行算法、数值计算及材料多尺度算法等" ]
  [ "白鹤（1992- ），男，北京科技大学计算机与通信工程学院博士生，主要研究方向为并行算法、数值计算等" ]
  [ "李慧昭（1999- ），男，北京科技大学计算机与通信工程学院硕士生，主要研究方向为计算机科学与技术、机器学习等" ]
  [ "何远杰（1999- ），男，北京科技大学计算机与通信工程学院硕士生，主要研究方向为计算机科学与技术、大数据等" ]
  [ "胡长军（1963- ），男，博士，北京科技大学计算机与通信工程学院教授、博士生导师。智能超算融合应用技术教育部工程研究中心主任，北京科技大学学术委员会委员，北京科技大学计算机科学技术学科负责人，北京市重点学科计算机系统结构负责人，计算机学会高性能计算专业委员会委员，Journal Citation Reports等国际期刊客座编辑。曾任清华大学信息技术研究院特聘研究员、中国原子能科学研究院特聘专家，主要研究方向为大数据工程及计算智能、超级计算机体系结构及系统软件、大规模并行应用软件系统" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(U1867217）)
- DOI：10.11959/j.issn.2096-0271.2021056
  中图分类号： TP391.4
- 网络首发：2021-11，
  
  纸质出版：2021-11-15
- 稿件说明：
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任帅, 陈丹丹, 储根深, 等. 基于材料数值计算大数据的材料辐照机理发现[J]. 大数据, 2021,7(6):3-18.

Shuai REN, Dandan CHEN, Genshen CHU, et al. Discovery of irradiation mechanism based on big data of material simulation[J]. Big data research, 2021, 7(6): 3-18.
任帅, 陈丹丹, 储根深, 等. 基于材料数值计算大数据的材料辐照机理发现[J]. 大数据, 2021,7(6):3-18. DOI： 10.11959/j.issn.2096-0271.2021056.

Shuai REN, Dandan CHEN, Genshen CHU, et al. Discovery of irradiation mechanism based on big data of material simulation[J]. Big data research, 2021, 7(6): 3-18. DOI： 10.11959/j.issn.2096-0271.2021056.

摘要

材料辐照效应的数值模拟计算是认识核材料服役性能的重要手段，基于超级计算机的大规模、高保真材料数值模拟计算会产生海量数值计算数据，如何针对数值计算大数据的特点，在实现其高效存储的基础上，通过挖掘总结辐照损伤机理和性能演化规律，对于核材料设计研发、核安全等具有重要意义。论述了材料数值计算大数据的定义及其本质特征，综述了近年来的相关工作。以自主研发的材料辐照效应分子动力学软件MISA-MD和随机团簇动力学软件MISA-SCD在国产超级计算机上的实际算例为基础，提出了一种适用于材料数值计算大数据的、多尺度关联与耦合的分布式数值计算大数据存储体系（NDSA）；采用XGBoost算法实现了MD中Frenkel缺陷对数的精确预测，基于并查集算法实现了级联碰撞团簇的划分；基于密度聚类的方法对KMC数值计算大数据进行挖掘，发现了类环状团簇，实现了原子团簇的识别与分类；基于第一性原理数值计算大数据库对现有的势函数模型进行了改进，提出了新的势函数模型构建方法AIPM。最后对材料数值计算大数据的应用前景进行了展望。

Abstract

The numerical simulation of material irradiation effect is an important means to understand the performance of nuclear materials.The large-scale and high fidelity material numerical simulation based on supercomputer will produce a large amount of numerical calculation data.Understanding the evolution law of the irradiation damage mechanism and performance through mining and analysis based on high-efficiency storage is of great significance for the design and development of nuclear materials and nuclear safety according to the characteristics of numerical calculation big data.The concept of big data of material simulation (MSBD) was proposed

and then the characteristics and significance of MSBD were specifically introduced

and the related work was reviewed.Based on the practical examples of MISA-MD and MISA-SCD on domestic supercomputers

a distributed numerical data storage arihitecture (NDSA) multi-scale correlation and coupling was proposed.Frenkel defect pairs were accurately calculated with XGBoost algorithm based on MSBD of MD

and the cascade collision clusters were artificially divided with Union-Find algorithm.The data of KMC numerical calculation were mined based on density clustering method

and the cluster recognition and classification were realized.The ring like clusters were found from MSBD of KMC based on density clustering algorithm

which was verified with the literature.A DNN-based potential model - AIPM was proposed with MSBD of first principles-based potential data.The further application of MSBD was discussed and prospected in physical modeling and knowledge discovery.

关键词

Keywords

references

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