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基于机器学习的反向设计方法,考虑材料设计和制造中的数据特征和设计空间大小:一篇回顾
Junhyeong Lee1, Donggeun Park1, Mingyu Lee1
1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. ryush@kaist.ac.kr.
Materials horizons
|August 10, 2023
概括
本综述指导材料科学家选择用于材料设计的机器学习 (ML) 算法. 它对问题进行了分类,并推了适合的ML模型,以加快新材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 显著影响材料科学,加速发现和生产.
- 复杂的ML模型需要在材料设计中选择算法的指导方针.
- 现有的理论往往不完全描述物理过程,强调了ML的预测能力.
研究的目的:
- 为材料设计提供常用ML算法的全面审查.
- 根据设计问题的特点,为选择合适的ML模型提供指导方针.
- 专注于ML用于复合结构的反向设计,特别是增材制造.
主要方法:
- 将材料设计问题分为四类:插值,推算,多忠度数据集和小数据集.
- 对每个类别的成功基于ML的替代模型和设计方法的讨论.
- 审查有关材料设计中的ML应用的相关文献.
主要成果:
- 确定适合不同数据可用性和设计空间探索场景的ML算法.
- 证明了ML在预测复杂材料行为的有效性.
- 为逆向设计挑战突出成功的ML策略.
结论:
- 有效的ML算法选择对于高效的材料设计至关重要.
- 分类设计问题有助于选择正确的ML方法.
- 机器学习是加速发现先进材料的强大工具,特别是在复合结构中.
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