用迁移学习筛选分子调节剂，让钙钛矿太阳能电池效率突破26.91%
Transfer learning discovery of molecular modulators for perovskite solar cells
- 基于预训练深度网络的迁移学习框架，融合多种分子表征
- 虚拟筛选超7.9万种分子，预测准确率高且成本低
- 可解释性强，实验证实最优调节剂使电池效率达26.91%
高效分子调节剂的发现对推进钙钛矿太阳能电池（PSCs）至关重要，但受限于化学空间庞大及实验试错成本高昂。机器学习虽具潜力，但因数据稀缺和传统定量结构-性能关系（QSPR）模型局限而应用困难。本文提出一种基于预训练深度神经网络的化学信息迁移学习框架，可高精度预测分子调节剂对PSCs功率转换效率（PCE）的影响。通过系统性基准测试多种分子表示方法，实现对79,043种商用分子的低成本、高通量虚拟筛选。进一步结合可解释性技术可视化学习到的化学特征，并实验验证调节剂-钙钛矿相互作用。经框架筛选出的最优分子调节剂经实验验证，使PSCs的冠军效率达到26.91%。
原文摘要 · Abstract (English)
The discovery of effective molecular modulators is essential for advancing perovskite solar cells (PSCs), but the research process is hindered by the vastness of chemical space and the time-consuming and expensive trial-and-error experimental screening. Concurrently, machine learning (ML) offers significant potential for accelerating materials discovery. However, applying ML to PSCs remains a major challenge due to data scarcity and limitations of traditional quantitative structure-property relationship (QSPR) models. Here, we apply a chemical informed transfer learning framework based on pre-trained deep neural networks, which achieves high accuracy in predicting the molecular modulator's effect on the power conversion efficiency (PCE) of PSCs. This framework is established through systematical benchmarking of diverse molecular representations, enabling lowcost and high-throughput virtual screening over 79,043 commercially available molecules. Furthermore, we leverage interpretability techniques to visualize the learned chemical representation and experimentally characterize the resulting modulator-perovskite interactions. The top molecular modulators identified by the framework are subsequently validated experimentally, delivering a remarkably improved champion PCE of 26.91% in PSCs.
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