在材料科学中用于机器学习的数据量治理
Yue Liu1,2, Zhengwei Yang1, Xinxin Zou1
1School of Computer Engineering and Science, Shanghai University, Shanghai200444, China.
National science review
|June 16, 2023
概括
材料科学中的机器学习 (ML) 在有限的数据上扎. 本研究回顾了数据治理策略,并提出了一个领域知识集成的方法,以改善加速材料发现的ML模型性能.
科学领域:
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 对材料科学至关重要,有助于结构-活动关系分析,性能优化和材料设计.
- 将ML应用于材料科学的一个重大挑战是数据稀缺,导致特征空间维度和样本大小之间或模型参数和样本大小之间不匹配.
- 这种数据限制往往导致ML模型性能差.
研究的目的:
- 审查现有策略,以解决材料科学ML中的数据限制,包括特征减少,样本增量和专门的ML方法.
- 强调在数据量治理中平衡样本大小与特征维度或模型参数的重要性.
- 提出一个新的协同效应的数据量治理框架,结合材料领域的知识.
主要方法:
- 对解决材料数据稀缺问题的技术的文献综述 ML.
- 分析样本大小,特征空间和模型参数之间的相互作用.
- 开发一个数据量治理流程,整合材料领域的知识.
主要成果:
- 讨论了现有的方法,如特征减少和样本增量,以减轻数据限制的方法.
- 强调需要仔细考虑数据量与模型复杂性之间的平衡.
- 将材料领域的知识纳入机器学习数据治理方案显示出显著的优势.
结论:
- 有效的数据量治理对于材料科学中的ML应用成功至关重要.
- 结合数据治理与材料领域知识的协同方法可以显著提高ML模型的性能.
- 这项工作为生成高质量的数据提供了一条途径,加速了基于ML的材料设计和发现.
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