This paper presents a novel approach for predicting Power Conversion
Efficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD:
synergizing molecular Graphs and Language Descriptors for enhanced PCE
prediction. Due to the lack of high-quality experimental data, we collect a
dataset consisting of 500 pairs of OPV donor and acceptor molecules along with
their corresponding PCE values, which we utilize as the training data for our
predictive model. In this low-data regime, GLaD leverages properties learned
from large language models (LLMs) pretrained on extensive scientific literature
to enrich molecular structural representations, allowing for a multimodal
representation of molecules. GLaD achieves precise predictions of PCE, thereby
facilitating the synthesis of new OPV molecules with improved efficiency.
Furthermore, GLaD showcases versatility, as it applies to a range of molecular
property prediction tasks (BBBP, BACE, ClinTox, and SIDER), not limited to
those concerning OPV materials. Especially, GLaD proves valuable for tasks in
low-data regimes within the chemical space, as it enriches molecular
representations by incorporating molecular property descriptions learned from
large-scale pretraining. This capability is significant in real-world
scientific endeavors like drug and material discovery, where access to
comprehensive data is crucial for informed decision-making and efficient
exploration of the chemical space.

通过整合分子图像和语言描述符进行增强光伏转换效率（PCE）预测的一种名为 GLaD 的新方法，利用大规模语言模型（LLM）从广泛的科学文献中预训练来丰富分子结构表示，实现了对 PCE 的精确预测，并促进了合成具有改善效率的新型有机光伏分子的过程。GLaD 还展示了多功能性，适用于一系列分子属性预测任务，尤其是在低数据环境中，能够通过融入从大规模预训练中学习的分子属性描述来丰富分子表示，这在药物和材料发现等现实科学探索中具有重要意义。

GLaD：在有机光伏装置中，将分子图和语言描述相结合以提高功率转换效率预测

GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced  Power Conversion Efficiency Prediction in Organic Photovoltaic Devices

This work presents a set of optimal machine learning (ML) models to represent
the temporal degradation suffered by the power conversion efficiency (PCE) of
polymeric organic solar cells (OSCs) with a multilayer structure
ITO/PEDOT:PSS/P3HT:PCBM/Al. To that aim, we generated a database with 996
entries, which includes up to 7 variables regarding both the manufacturing
process and environmental conditions for more than 180 days. Then, we relied on
a software framework that brings together a conglomeration of automated ML
protocols that execute sequentially against our database by simply command-line
interface. This easily permits hyper-optimizing and randomizing seeds of the ML
models through exhaustive benchmarking so that optimal models are obtained. The
accuracy achieved reaches values of the coefficient determination (R2) widely
exceeding 0.90, whereas the root mean squared error (RMSE), sum of squared
error (SSE), and mean absolute error (MAE)>1% of the target value, the PCE.
Additionally, we contribute with validated models able to screen the behavior
of OSCs never seen in the database. In that case, R2~0.96-0.97 and RMSE~1%,
thus confirming the reliability of the proposal to predict. For comparative
purposes, classical Bayesian regression fitting based on non-linear mean
squares (LMS) are also presented, which only perform sufficiently for
univariate cases of single OSCs. Hence they fail to outperform the breadth of
the capabilities shown by the ML models. Finally, thanks to the standardized
results offered by the ML framework, we study the dependencies between the
variables of the dataset and their implications for the optimal performance and
stability of the OSCs. Reproducibility is ensured by a standardized report
altogether with the dataset, which are publicly available at Github.

通过使用机器学习模型，本研究提出了一种优化的方法来表示聚合物有机太阳能电池的功率转换效率的时间衰减。该方法应用于一个包含 996 个条目的数据库，并使用自动化机器学习协议进行优化，结果显示优化模型的确定系数达到超过 0.90，预测可靠性得到确认，并得出了关于数据集变量与 OSC 的性能和稳定性之间的关联性的结论。