自动MatRegressor:解放机器学习的炼金术士
Yue Liu1, Shuangyan Wang2, Zhengwei Yang2
1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; Shanghai Engineering Research Center of Intelligent Computing System, Shanghai 200444, China; Zhejiang Laboratory, Hangzhou 311100, China.
Science bulletin
|June 2, 2023
概括
自动MatRegressor通过自动化机器学习 (ML) 模型创建来加速材料属性预测. 这种方法使用meta-learning来选择最佳的算法和超参数,降低实验成本并提高准确性.
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
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 对于通过识别模式和关系来预测材料特性至关重要.
- 目前用于材料科学的ML模型开发受到耗时和劳动密集型实验数据采集的阻碍.
- 高精度的ML模型需要在算法选择和超参数调整方面拥有广泛的数据和专业知识.
研究的目的:
- 开发一种用于材料属性预测的自动机器学习 (ML) 建模方法.
- 为了降低计算成本和实验力度,为材料科学建立准确的ML模型.
- 介绍Auto-MatRegressor,这是一个基于meta-learning的方法,用于自动化算法选择和超参数优化.
主要方法:
- 开发了Auto-MatRegressor,这是一个自动建模框架,利用对历史材料数据集的元学习.
- 整合了27个表征数据集的元特征和18个常见材料科学算法的性能.
- 实施了一个协作型的超级学习策略,增强了来自材料类别树的域名知识,用于算法推.
主要成果:
- 与传统方法相比,Auto-MatRegressor证明了高效的算法选择和超参数优化.
- 自动化方法显著降低了计算成本,同时在60个不同的数据集中实现了良好的预测准确性.
- 该系统在加速用于材料属性预测的ML模型的构建方面表现出有效性.
结论:
- 自动MatRegressor为材料发现和设计中的自动化ML模型开发提供了可扩展和高效的解决方案.
- 超学习方法有效地利用过去的建模经验来指导新模型的构建.
- 这种方法支持动态扩展,随着更多的数据和算法变得可用,可以进行持续改进.
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