School of Microelectronics, Shanghai University, Shanghai 201800, China.
J Chem Inf Model. 2023 Sep 11;63(17):5473-5483. doi: 10.1021/acs.jcim.3c00326. Epub 2023 Aug 24.
The construction of material prediction models using machine learning algorithms can aid in the polyimide structural design and screening of materials as well as accelerate the development of new materials. There is a lack of research on predicting the optical properties of polyimide materials and the interpretation of the structural features. Here, we collected 652 polyimide molecular structures and used seven popular machine learning algorithms to predict the glass transition temperature and cut-off wavelength of polyimide materials and extract key feature information of repeating unit structures. The results showed that the root mean square error of the glass transition temperature prediction model was 33.92 °C, and the correlation coefficient was 0.861. The root mean square error of the cut-off wavelength prediction model was 17.18 nm, and the correlation coefficient was 0.837. The elasticity of the molecular structure was also found to be the key factor affecting glass transition temperature, and the presence and location of heterogeneous atoms had a significant effect on the cut-off wavelengths. Finally, eight polyimide materials were synthesized to test the accuracy of the prediction models, and the experimental characterization values agreed with the predicted values. The results would contribute to the development of polyimide structural design and materials preparation for flexible display.
利用机器学习算法构建材料预测模型，可以辅助聚酰亚胺结构设计和材料筛选，并加速新材料的开发。目前，对于聚酰亚胺材料光学性能的预测以及结构特征的解释研究还较少。本研究收集了 652 种聚酰亚胺分子结构，采用 7 种常用的机器学习算法，对聚酰亚胺材料的玻璃化转变温度和截止波长进行预测，并提取重复单元结构的关键特征信息。结果表明，玻璃化转变温度预测模型的均方根误差为 33.92°C，相关系数为 0.861；截止波长预测模型的均方根误差为 17.18nm，相关系数为 0.837。分子结构的弹性也被发现是影响玻璃化转变温度的关键因素，杂原子的存在和位置对截止波长有显著影响。最后，合成了 8 种聚酰亚胺材料进行预测模型的准确性验证，实验特征值与预测值吻合较好。该研究结果将有助于推动聚酰亚胺结构设计和柔性显示用材料制备的发展。
