通过机器学习预测小铁多晶体的弹性和塑性
Marcin Mińkowski1, Lasse Laurson2
1Computational Physics Laboratory, Tampere University, P.O. Box 692, FI-33014, Tampere, Finland. marcin.minkowski@tuni.fi.
Scientific reports
|August 26, 2023
概括
使用机器学习可以预测铁多晶体的机械性质. 研究人员发现,基于初始结构的剪切模量比收益应力更可预测.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 固体力学 固体力学是什么
背景情况:
- 晶体材料变形表现出复杂的系统行为.
- 小样本显示了随机反应和机械性质的显著变化.
- 了解这种变化对于材料设计和应用至关重要.
研究的目的:
- 研究小铁多晶体中机械性质 (切割模和屈服应力) 的可预测性.
- 为了确定初始多晶结构和机械反应之间的相关性.
- 评估机器学习在预测材料行为的有效性.
主要方法:
- 利用沃罗诺伊图片制造来产生一大组小的,立方形的铁多晶体.
- 采用分子动力学模拟来获得每个样本的应力-应变曲线.
- 训练了一个卷积神经网络,将微观结构特征映射到机械特性.
主要成果:
- 卷积神经网络成功地预测了切割模量,准确度高于产应力.
- 证明了初始多晶结构与机械性能之间的可量化的关系.
- 突出了材料对初始结构扰动的反应的敏感性.
结论:
- 机器学习,特别是卷积神经网络,可以从它们的结构中预测晶体材料的机械性质.
- 剪切模量预测比收益率应力预测更强大,这是由于潜在的物理机制.
- 对于复杂材料,对变形对初始条件的敏感性进行进一步的研究是有必要的.
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