利用机器学习来巩固矿太阳能电池实验结果的多样性
Wahid Hussain1, Samina Sawar2, Muhammad Sultan3
1Department of Physics, Quaid-i-Azam University 45320 Islamabad Pakistan wahidhussaiin@gmail.com.
RSC advances
|July 27, 2023
概括
机器学习模型预测混合有机-无机矿带间隙和太阳能电池效率. 这使矿太阳能电池的开发自动化,在没有试错的情况下优化性能.
科学领域:
- 材料科学 材料科学 材料科学
- 可再生能源可再生能源是可再生能源.
- 计算化学的计算化学
背景情况:
- 矿太阳能电池 (PSC) 由于灵活性和解决方案可加工性,显示出对智能能源的承诺.
- 基于溶液的技术产生不一致的制造结果,阻碍了可靠的设备开发.
- 使用机器学习 (ML) 的自动设计可以在PSC中解决这些挑战.
研究的目的:
- 开发ML模型来预测混合有机-无机矿 (HOIP) 的带隙.
- 为了预测基于HOIP的太阳能电池的功率转换效率 (PCE).
- 为了实现矿太阳能电池的自动设计和优化.
主要方法:
- 分析了42,000个实验数据集.
- 开发用于矿装置设计的两步ML预测方法.
- 使用Shockley-Queisser模型对设备参数的带隙依赖性的验证.
主要成果:
- 成功开发了用于预测HOIP带间隙的ML模型.
- 实现了对矿太阳能电池功率转换效率的准确预测.
- 证明了矿材料开发和设备优化的自动化.
结论:
- ML提供了一种强大的方法来克服PSC的制造变化.
- 开发的模型简化了矿太阳能电池的开发和优化.
- 这种数据驱动的策略减少了对耗时的实验试验和错误的依赖.
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