基于神经网络的阶段过渡顺序参数及其在高合金中的应用
Junqi Yin1, Zongrui Pei2,3, Michael C Gao4
1Oak Ridge National Laboratory, Oak Ridge, TN, USA. yinj@ornl.gov.
Nature computational science
|January 13, 2024
概括
研究人员开发了一种新的变异自编码器 (VAE) 方法,用于识别材料科学中的通用顺序参数,特别是复杂合金中的顺序-失序相过渡. 这种方法有助于理解和设计先进的材料.
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
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 阶段过渡是材料特性和设计的基础.
- 顺序参数对于描述相位过渡至关重要.
- 对于像高合金这样的复杂系统来说,确定顺序参数是一项挑战.
研究的目的:
- 引入一种新的,通用的顺序参数,用于顺序-失序阶段过渡.
- 为了利用变化自编码器 (VAE) 进行材料设计.
- 开发一种计算技术,以了解合金中的化学排序.
主要方法:
- 使用一个变量自编码器 (VAE) 来减小维度.
- 提出了曼哈顿距离在VAE潜伏空间作为一个通用的顺序参数.
- 使用耐火高合金验证了顺序参数.
主要成果:
- 证明了基于VAE的订单参数的定量解释和物理特性.
- 展示了顺序参数在多重耐火高合金中的有效性.
- 建立了曼哈顿距离在VAE潜伏空间作为一个可行的顺序参数.
结论:
- 基于VAE的顺序参数为分析化学排序提供了一种计算效率高的方法.
- 提出了一个普遍适用的合金设计概念,模仿自然元素的混合.
- 这种方法促进了对先进材料的合理合金设计策略的开发.
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