在相场预测中为机器学习设定标准:一个基准数据集和基线指标
Laura Hannemose Rieger1, Klemen Zelič2,3, Igor Mele2,3
1Department of Energy and Conversion Storage, Technical University of Denmark (DTU), Lyngby, 2800 Kgs, Denmark. lauri@dtu.dk.
Scientific data
|November 23, 2024
概括
本研究引入了一个新的数据集,用于在相场模拟中对机器学习算法进行基准测试. 该数据集可以更快地开发用于微观结构分析的AI模型.
科学领域:
- 计算材料科学科学 计算材料科学
- 中等尺度建模模型
- 机器学习应用 机器学习应用
背景情况:
- 阶段场模型是模拟微观结构演变的重要中等尺度工具.
- 通过机器学习 (ML) 加快这些模拟需要广泛的标准化数据集.
- 目前的ML算法开发受到缺乏可访问的基准测试数据的阻碍.
研究的目的:
- 引入一种新的,记录良好的数据集,用于在相场建模中对ML算法进行基准测试.
- 提供一个资源,促进新的ML方法的开发和比较.
- 通过基准研究验证数据集的实用性.
主要方法:
- 开发一个可访问和记录的数据集,用于相场模拟.
- 使用U-Net回归进行基准测试,这是一个卷积神经网络架构.
- 在多个域大小中验证以评估概括性.
主要成果:
- U-Net回归基准实现了具有竞争力的错误指标.
- 该模型展示了跨不同模拟域大小的概括能力.
- 该数据集被证明是有效的,用于验证ML在相场环境中的性能.
结论:
- 引入的数据集是推动ML在相场模拟中的宝贵资源.
- U-Net回归显示了加速和改进微结构分析的前景.
- 这项工作突出了在这一领域开发新型ML方法的机会.
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