基于基于物理知识的特征的溶液分离能量的高效机器学习.
1Guangxi Education Department Key Laboratory of Microelectronic Packaging and Assembly Technology, School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China.
Scientific reports
|July 15, 2023
概括
这项研究为机器学习模型引入了基于物理的特性,用于预测溶液分离能量. 与现有方法相比,这些特性提供了更高的准确性和维度平衡.
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
- 机器学习 机器学习
背景情况:
- 机器学习模型使用站点特征预测溶液分离能量.
- 块状特征可以导致维度的诅咒,影响模型性能.
- 现有的方法往往缺乏准确性和特征集大小之间的平衡.
研究的目的:
- 开发高效的机器学习模型来预测分离能量.
- 识别和利用基于物理的特征,以提高模型性能.
- 证明将物理理解整合到机器学习的特征选择中的优势.
主要方法:
- 模拟的分离能量使用机器学习与物理信息的功能.
- 根据对隔离地点的基本物理理解来确定特征.
- 与文献特征和光谱邻近分析潜力 (SNAP) 特征进行性能比较.
主要成果:
- 基于物理学的特征在精度和特征尺寸之间取得了更好的平衡.
- 这些功能在不同的机器学习算法和合金系统中表现优于现有方法.
- 选择的特征显示出与分离能量和因其物理基础而相互独立的强烈相关性.
结论:
- 将物理整合到特征识别中可以提高机器学习模型的效率和准确性,用于隔离能量预测.
- 与仅使用能量计算相比,基于物理的功能减少了冗余信息.
- 这种方法突显了物理引导的特征工程在机器学习应用中的价值.
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