基于物理的机器学习与数据驱动的方程,用于预测有机太阳能电池性能
Rudranarayan Khatua1, Bibhas Das1, Anirban Mondal1
1Department of Chemistry, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382355, India.
ACS applied materials & interfaces
|October 10, 2024
概括
这项研究通过将量子力学描述符与基于物理的机器学习相结合,推进有机太阳能电池 (OSC). 开发的模型准确地预测OSC性能,加速发现可持续能源材料.
科学领域:
- 材料科学 材料科学 材料科学
- 可持续能源 可持续能源
- 计算化学计算化学
背景情况:
- 有机太阳能电池 (OSC) 为可持续能源解决方案提供了一个有希望的途径.
- 传统的OSC开发实验方法面临速度和范围的限制.
- 将理论计算与机器学习相结合,可以加速材料的发现.
研究的目的:
- 为有机太阳能电池 (OSC) 性能开发先进的预测模型.
- 利用量子力学 (QM) 描述器和基于物理的机器学习 (PIML) 来加强OSC开发.
- 确定控制光伏性能的关键描述因素,并加速材料发现.
主要方法:
- 使用高通量量子力学 (QM) 计算来生成数据.
- 使用SISSO++方法确定关键描述符,将输入变量与性能相关联.
- 开发了基于物理的机器学习 (PIML) 模型,并使用新的数据集进行了验证.
主要成果:
- 开发的模型准确地预测了OSC关键参数:短路电流 (JSC),开路电压 (VOC),填充因子 (FF) 和最大功率转换效率 (PCEmax).
- 即使使用有限的数据集,也可以实现高精度,证明了模型的稳定性.
- 在PIML框架成功地确定了用于OSC应用的高性能材料.
结论:
- 质量管理描述符和PIML模型的综合方法显著推进了OSC的发展.
- 这些模型弥合了理论预测和实验结果之间的差距,加速了可持续能源技术的发展.
- 这项研究强调了数据驱动的,可解释的模型在发现新的,高性能的OSC材料方面的潜力.
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