Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters.

Among various methods for fabricating polymeric tissue engineering scaffolds, electrospinning stands out as a relatively simple technique widely utilized in research. Numerous studies have delved into understanding how electrospinning processing parameters and specific polymeric solutions affect the physical features of the resulting scaffolds. However, owing to the complexity of these interactions, no definitive approaches have emerged. This study introduces the use of Simplified Molecular Input Line Entry System (SMILES) encoding method to represent materials, coupled with machine learning algorithms, to model the relationships between material properties, electrospinning parameters and scaffolds' physical properties. Here, the scaffolds' fiber diameter and conductivity have been predicted for the first time using this approach. In the classification task, the voting classifier predicted the fibers diameter with a balanced accuracy score of 0.9478. In the regression task, a neural network regressor was architected to learn the relations between parameters and predict the fibers diameter with R2 = 0.723. In the case of fibers conductivity, regressor and classifier models were used for prediction, but the performance fluctuated due to the inadequate information in the published data and the collected dataset. Finally, the model prediction accuracy was validated by experimental electrospinning of a biocompatible polymer (i.e., polyvinyl alcohol and polyvinyl alcohol/polypyrrole). Field-emission scanning electron microscope (FE-SEM) images were used to measure fiber diameter. These results demonstrated the efficacy of the proposed model in predicting the polymer nanofiber diameter and reducing the parameter space prior to the scoping exercises. This data-driven model can be readily extended to the electrospinning of various biopolymers.

Performance Analysis of Anode-Supported Solid Oxide Fuel Cells: A Machine Learning Approach.

Prior to the long-term utilization of solid oxide fuel cell (SOFC), one of the most remarkable electrochemical energy conversion devices, a variety of difficult experimental validation procedures is required, so it would be time-consuming and steep to predict the applicability of these devices in the future. For numerous years, extensive efforts have been made to develop mathematical models to predict the effects of various characteristics of solid oxide fuel cells (SOFCs) components on their performance (e.g., voltage). Taking advantage of the machine learning (ML) method, however, some issues caused by assumptions and calculation costs in mathematical modeling could be alleviated. This paper presents a machine learning approach to predict the anode-supported SOFCs performance as one of the most promising types of SOFCs based on architectural and operational variables. Accordingly, a dataset was collected from a study about the effects of cell parameters on the output voltage of a Ni-YSZ anode-supported cell. Convolutional machine learning models and multilayer perceptron neural networks were implemented to predict the current-voltage dependency. The resulting neural network model could properly predict, with more than 0.998 R2 score, a mean squared error of 9.6 × 10-5, and mean absolute error of 6 × 10-3 (V). Conventional models such as the Gaussian process as one of the most powerful models exhibits a prediction accuracy of 0.996 R2 score, 10-4 mean squared, and 6 × 10-3 (V) absolute error. The results showed that the built neural network could predict the effect of cell parameters on current-voltage dependency more accurately than previous mathematical and artificial neural network models. It is noteworthy that this procedure used in this study is general and can be easily applied to other materials datasets.