Eibeck Andreas, Nurkowski Daniel, Menon Angiras, Bai Jiaru, Wu Jinkui, Zhou Li, Mosbach Sebastian, Akroyd Jethro, Kraft Markus
CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, 138602 Singapore.
CMCL Innovations, Sheraton House, Castle Park, Cambridge CB3 0AX, U.K.
ACS Omega. 2021 Sep 6;6(37):23764-23775. doi: 10.1021/acsomega.1c02156. eCollection 2021 Sep 21.
In this paper, the ability of three selected machine learning neural and baseline models in predicting the power conversion efficiency (PCE) of organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentive fingerprints (attentive FP), and simple graph neural networks (simple GNN) as well as baseline support vector regression (SVR), random forests (RF), and high-dimensional model representation (HDMR) methods are trained to both the large and computational Harvard clean energy project database (CEPDB) and the much smaller experimental Harvard organic photovoltaic 15 dataset (HOPV15). It was found that the neural-based models generally performed better on the computational dataset with the attentive FP model reaching a state-of-the-art performance with the test set mean squared error of 0.071. The experimental dataset proved much harder to fit, with all of the models exhibiting a rather poor performance. Contrary to the computational dataset, the baseline models were found to perform better than the neural models. To improve the ability of machine learning models to predict PCEs for OPVs, either better computational results that correlate well with experiments or more experimental data at well-controlled conditions are likely required.
在本文中，评估了三种选定的机器学习神经网络模型和基线模型利用分子结构信息作为输入来预测有机光伏电池（OPV）功率转换效率（PCE）的能力。双向长短期记忆网络（gFSI/BiLSTM）、注意力指纹（attentive FP）、简单图神经网络（simple GNN）以及基线支持向量回归（SVR）、随机森林（RF）和高维模型表示（HDMR）方法，在大型计算型哈佛清洁能源项目数据库（CEPDB）和小得多的实验型哈佛有机光伏15数据集（HOPV15）上进行了训练。结果发现，基于神经网络的模型在计算数据集上通常表现更好，注意力指纹模型达到了先进水平，测试集均方误差为0.071。实验数据集被证明更难拟合，所有模型的表现都相当差。与计算数据集相反，发现基线模型比神经网络模型表现更好。为了提高机器学习模型预测OPV的PCE的能力，可能需要与实验结果高度相关的更好的计算结果，或者在严格控制条件下的更多实验数据。
