机器学习辅助预测和控制有机-无机金属化物罗夫斯基特的带隙
Fuchun Gou1, Zhu Ma1,2,3, Qiang Yang1
1School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China.
ACS applied materials & interfaces
|March 14, 2025
概括
本研究引入了一个异常值去除策略,以改进用于预测矿带间隙的机器学习模型. 梯度增强回归树算法实现了高精度,识别了带隙控制的关键元素比率.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 矿材料为各种应用提供可调节的带间隙.
- 机器学习加速了新材料的发现.
- 数据集中的数据噪声阻碍了传统的预测模型.
研究的目的:
- 开发一个异常值去除策略,以提高机器学习模型的概括性.
- 确定用于预测模拟矿带间隙的最佳配置.
- 确定影响矿带间隙的关键化学成分因素.
主要方法:
- 实施了消除异常值的策略,以评估其对模型性能的影响.
- 使用梯度增强回归树 (GBRT) 算法进行预测.
- 使用沙普利增量解释 (SHAP) 方法来解释模型预测.
主要成果:
- GBRT算法实现了高精度,MAE为0.0287,MSE为0.0014,RMSE为0.0377,R平方为0.979.
- SHAP分析显示, (I) 的比率显著影响了带隙,其次是 (Pb), (Br) 和锡 (Sn) 的比率.
- 实验验证证了元素比稳定性边界对于频段间隙控制至关重要.
结论:
- 一个有效的异常值去除策略可以提高机器学习模型对矿带间隙预测的准确性.
- GBRT算法在预测矿带间隙方面表现出卓越的性能.
- 了解化学成分比率的影响,特别是,对于精确的矿带隙工程至关重要.
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