通过可解释机器学习加速发现具有有针对性的带间隙的混合矿
Chao Yang1, Xiaoyu Chong1, Mingyu Hu2
1Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China.
ACS applied materials & interfaces
|August 18, 2023
概括
本研究引入了一种可解释的机器学习策略,以准确预测和理解影响混合有机-无机矿带间隙的因素,从而实现高效太阳能电池的合理设计.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 可再生能源可再生能源是可再生能源.
背景情况:
- 混合有机-无机矿 (HOIPs) 的带隙极大地影响了矿太阳能电池 (PSC) 的性能.
- 目前的带隙工程方法通常是低效和昂贵的,依赖于试错或广泛的计算.
研究的目的:
- 开发一种可解释的机器学习 (ML) 策略,用于准确预测HOIP频段差距.
- 识别和量化影响HOIP带间隙的关键因素.
- 为了使HOIP的合理设计能够具有有针对性的频段间隙.
主要方法:
- 结合特征工程与梯度增强回归树 (GBRT) 和基于遗传算法的符号回归 (GASR) 算法.
- 选择了七个关键的物理特征用于模型的构建.
- 开发了一个可解释的ML模型用于定量分析.
主要成果:
- 实现了高精度的GBRT模型,其根-平方平均误差低于0.060 eV.
- 确定了B位和X位 (χB-X) 之间的电子阴性差异是最有影响力的特征.
- 来自一个数学公式 (Eg = χB-X2 + 0.881χB-X) 用于定量带间隙解释.
- 成功设计了一个HOIP (MA0.23FA0.02Cs0.75Pb0.59Sn0.41Br0.24I2.76) 的目标带间隙为1.39 eV.
结论:
- 拟议的可解释的ML策略为设计具有所需带间隙的HOIP提供了一种有效的方法.
- 这种方法可以加速用于太阳能应用的先进材料的开发.
- 这种方法可能适用于其他材料设计挑战.
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