STUDY OF THE TEMPERATURE DEPENDENCE OF THE SYMMETRICAL GRAIN BOUNDARY ENERGIES ON THE PLANE (110) IN ALUMINUM

Q4 Mathematics
E.V. Fomin
{"title":"STUDY OF THE TEMPERATURE DEPENDENCE OF THE SYMMETRICAL GRAIN BOUNDARY ENERGIES ON THE PLANE (110) IN ALUMINUM","authors":"E.V. Fomin","doi":"10.47475/2500-0101-2023-8-3-421-435","DOIUrl":null,"url":null,"abstract":"In this work the energy of symmetric tilt and twist grain boundaries in the range of grain misorientation angles from 0 to 180◦ and temperatures from 100 to 700 K in pure aluminum is investigated. The bicrystal systems with different grain tilt/twist angles are maintained at constant temperatures of 100, 400, or 700 K by molecular dynamic method and the energy of each grain boundary is calculated. The results show that the minimum grain boundary energy decreases as the temperature increases from 100 to 400 K; but the energy may decrease, remain practically unchanged, or even increase with further heating to 700 K. The average grain boundary energy obtained by averaging the energies of the resulting grain boundary structure variations at constant temperature grows with increasing temperature from 100 to 700 K for random boundaries with initially high energies. In the case of special grain boundaries with small Σ values, the average energy will be practically unchanged. To describe the continuous energy dependence of symmetric tilt and twist boundaries on temperature, an approximation by an forward propagation of artificial neural network is proposed. The neural network is trained and tested on atomistic simulation data and shows high predictive ability on test data and to describe the boundary energy in the temperature range from 100 to 700 K.","PeriodicalId":36654,"journal":{"name":"Chelyabinsk Physical and Mathematical Journal","volume":"193 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chelyabinsk Physical and Mathematical Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47475/2500-0101-2023-8-3-421-435","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

Abstract

In this work the energy of symmetric tilt and twist grain boundaries in the range of grain misorientation angles from 0 to 180◦ and temperatures from 100 to 700 K in pure aluminum is investigated. The bicrystal systems with different grain tilt/twist angles are maintained at constant temperatures of 100, 400, or 700 K by molecular dynamic method and the energy of each grain boundary is calculated. The results show that the minimum grain boundary energy decreases as the temperature increases from 100 to 400 K; but the energy may decrease, remain practically unchanged, or even increase with further heating to 700 K. The average grain boundary energy obtained by averaging the energies of the resulting grain boundary structure variations at constant temperature grows with increasing temperature from 100 to 700 K for random boundaries with initially high energies. In the case of special grain boundaries with small Σ values, the average energy will be practically unchanged. To describe the continuous energy dependence of symmetric tilt and twist boundaries on temperature, an approximation by an forward propagation of artificial neural network is proposed. The neural network is trained and tested on atomistic simulation data and shows high predictive ability on test data and to describe the boundary energy in the temperature range from 100 to 700 K.
铝平面(110)上对称晶界能温度依赖性的研究
本文研究了纯铝在0 ~ 180°取向角和100 ~ 700 K温度范围内对称倾斜晶界和扭转晶界的能量。采用分子动力学方法将具有不同晶粒倾斜/扭转角度的双晶体系维持在100、400和700 K的恒温下,并计算每个晶界的能量。结果表明:在100 ~ 400 K温度范围内,最小晶界能随温度的升高而减小;但当进一步加热到700k时,能量可能会减少,几乎保持不变,甚至增加。对于初始能量较高的随机晶界,在恒定温度下对晶界结构变化的能量进行平均计算得到的平均晶界能随着温度的升高而增大。在Σ值较小的特殊晶界情况下,平均能量几乎不变。为了描述对称倾斜和扭转边界对温度的连续能量依赖,提出了一种人工神经网络前向传播的逼近方法。该神经网络在原子模拟数据上进行了训练和测试,对实验数据和描述100 ~ 700 K温度范围内的边界能表现出较高的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Chelyabinsk Physical and Mathematical Journal
Chelyabinsk Physical and Mathematical Journal Mathematics-Mathematics (all)
CiteScore
0.90
自引率
0.00%
发文量
11
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信