使用机器学习预测无机化合物的折射率
Elham Einabadi1, Mahdi Mashkoori2,3
1Department of Physics, K.N. Toosi University of Technology, P. O. Box 15875-4416, Tehran, Iran.
Scientific reports
|October 15, 2024
概括
机器学习使用带间隙和原子性质准确预测材料折射率 (RI). 极端随机树回归 (ERTR) 显示了低成本RI估计的最高准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 光学是什么?光学是什么?光学是什么?
背景情况:
- 折射率 (RI) 是材料的关键光学属性.
- 准确的RI预测对于材料设计和应用至关重要.
- 现有的RI估计方法可能是计算密集的.
研究的目的:
- 开发一个具有成本效益的机器学习模型来预测材料折射率.
- 为了确定准确的RI估计的关键预测因素.
- 为了比较RI预测的各种回归算法的性能.
主要方法:
- 利用272种无机化合物的实验测量RI值.
- 采用带间隙和原子属性作为预测特征.
- 研究的特征集有1,5,10和21个预测器.
- 我们比较了六种回归方法:OLSR,GPR,SVR,RFR,GBTR和ERTR.
主要成果:
- 极端随机树回归 (ERTR) 证明了最高的预测准确性.
- 机器学习模型在广泛范围内提供准确的RI估计.
- 该模型的预测强度超过了传统的经验关系.
结论:
- 机器学习为评估材料折射率提供了一种强大而高效的方法.
- 带间隙和原子性质是RI的重要预测因素.
- 使用这种方法,ERTR是一种高效的RI预测算法.
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