We present a study on Explainable AI-based prediction of power conversion efficiency (PCE) of organic solar cells, conducted on a dataset of 566 small-molecule organic solar cell materials samples with varying donor and acceptor species combinations. This research uncovers an interesting phenomenon, the first of its kind to be reported, of PCE quantization, where the PCE values increase in steps with the increase in feature values. Our findings have significant implications for the development of efficient organic solar cells, as they provide a better understanding of the factors that influence PCE, and highlight the feature value ranges for which more efficient PCE would be achieved. Our study demonstrates the power of XAI techniques in uncovering hidden patterns in scientific datasets and highlights the importance of interdisciplinary research in the field of materials science.

中文翻译：

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使用可解释的人工智能增强光伏材料太阳能转换效率的预测模型

我们提出了一项基于可解释人工智能的有机太阳能电池功率转换效率 (PCE) 预测的研究，该研究在 566 个具有不同供体和受体物种组合的小分子有机太阳能电池材料样本的数据集上进行。这项研究发现了一个有趣的现象，这是首次报道的 PCE 量化现象，即 PCE 值随着特征值的增加而逐步增加。我们的研究结果对高效有机太阳能电池的开发具有重要意义，因为它们可以更好地理解影响 PCE 的因素，并强调可以实现更高效 PCE 的特征值范围。我们的研究证明了 XAI 技术在揭示科学数据集中隐藏模式方面的力量，并强调了材料科学领域跨学科研究的重要性。