Luo Quan, Hao Hua, Liu Hanxing
State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Center for Smart Materials and Device, International School of Materials Science and Engineering, Wuhan University of Technology, Wuhan, PR 430070, People's Republic of China.
R Soc Open Sci. 2024 May 29;11(5):231464. doi: 10.1098/rsos.231464. eCollection 2024 May.
The perovskite crystal structure represents a semiconductor material poised for widespread application, underpinned by attributes encompassing heightened efficiency, cost-effectiveness and remarkable flexibility. Notably, strontium titanate (SrTiO)-type perovskite, a prototypical ferroelectric dielectric material, has emerged as a pre-eminent matrix material for enhancing the energy storage capacity of perovskite. Typically, the strategy involves augmenting its dielectric constant through doping to enhance energy storage density. However, SrTiO doping data are plagued by significant dispersion, and the small sample size poses a formidable research hurdle, hindering the investigation of dielectric property and energy storage density enhancements. This study endeavours to address this challenge, our foundation lies in the compilation of 200 experimental records related to SrTiO-type perovskite doping, constituting a small dataset. Subsequently, an interactive framework harnesses deep neural network models and a one-dimensional convolutional neural network model to predict and scrutinize the dataset. Distinctively, the mole percentage of doping elements exclusively serves as input features, yielding significantly enhanced accuracy in dielectric performance prediction. Lastly, rigorous comparisons with traditional machine learning models, specifically gradient boosting regression, validate the superiority and reliability of deep learning models. This research advances a novel, effective methodology and offers a valuable reference for designing and optimizing perovskite energy storage materials.
钙钛矿晶体结构代表了一种有望广泛应用的半导体材料，其具有高效、成本效益高和显著灵活性等特性。值得注意的是，钛酸锶（SrTiO）型钙钛矿作为一种典型的铁电介电材料，已成为提高钙钛矿储能能力的卓越基体材料。通常，该策略包括通过掺杂提高其介电常数以增强储能密度。然而，SrTiO掺杂数据存在显著的分散性，且样本量小构成了巨大的研究障碍，阻碍了对介电性能和储能密度增强的研究。本研究致力于应对这一挑战，我们的基础是汇编了200条与SrTiO型钙钛矿掺杂相关的实验记录，构成了一个小数据集。随后，一个交互式框架利用深度神经网络模型和一维卷积神经网络模型对该数据集进行预测和审查。独特的是，掺杂元素的摩尔百分比仅用作输入特征，在介电性能预测中产生了显著提高的准确性。最后，与传统机器学习模型（特别是梯度提升回归）进行的严格比较，验证了深度学习模型的优越性和可靠性。本研究提出了一种新颖、有效的方法，并为设计和优化钙钛矿储能材料提供了有价值的参考。
