梯度增强和统计特征选择工作流程用于材料属性预测
Son Gyo Jung1,2,3, Guwon Jung1,3,4, Jacqueline M Cole1,2,3
1Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom.
The Journal of chemical physics
|November 16, 2023
概括
本研究引入了一种用于材料科学中的机器学习的新型特征选择工作流. 它有效地识别相关特征,降低计算成本并提高模型准确性,以加速材料发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 数据驱动的方法和机器学习 (ML) 对于加速材料发现至关重要.
- 当前的ML方法往往缺乏可解释性,并且需要计算上昂贵的规范化技术.
- 识别相关特征是改善ML模型概括和降低计算成本的关键.
研究的目的:
- 开发和验证材料科学中的ML特征选择工作流程.
- 为了提高模型的可解释性和降低计算成本.
- 通过高效的数据分析,加速新材料的发现.
主要方法:
- 一个递归的特征选择工作流,利用梯度增强和统计分析.
- 使用特征相关性和层次聚类来减少多对线性.
- 包装方法与贪的搜索功能改进.
- 贝叶斯优化用于ML模型训练.
主要成果:
- 工作流成功地识别了相关的特征子集,最大限度地提高了预测能力.
- 最小的特征冗余性是通过多线性减少实现的.
- 在没有规范化的情况下,使用所选功能进行训练的ML模型实现了最先进的性能.
- 工作流证明了在各种材料属性预测中具有普遍性.
结论:
- 拟议的特征选择工作流程有效地解决了材料发现中的ML解释性和计算成本挑战.
- 这种方法可以开发准确和高效的ML模型来预测材料特性.
- 工作流程有助于加速发现新材料.
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