RESUMO
Cracks that form during fatigue offer critical information regarding the fracture process of the associated material, such as the crack speed, energy dissipation, and material stiffness. Characterization of the surfaces formed after these cracks have propagated through the material can provide important information complementary to other in-depth analyses. However, because of the complex nature of these cracks, their characterization is difficult, and most of the established characterization techniques are inadequate. Recently, Machine Learning techniques are being applied to image-based material science problems in predicting structure-property relations. Convolutional neural networks (CNNs) have proven their capacity on modeling complex and diverse images. The downside of CNNs for supervised learning is that that they require large amounts of training data. One work-around is using a pre-trained model, i.e., transfer learning (TL). However, TL models cannot be used directly without modification. In this paper, to use TL for crack surface feature-property mapping, we propose to prune the pre-trained model to retain the weights of the first several convolutional layers. Those layers are then used to extract relevant underlying features from the microstructural images. Next, principal component analysis (PCA) is used to further reduce the feature dimension. Finally, the extracted crack features together with the temperature effect are correlated with the properties of interest using regression models. The proposed approach is first tested on artificial microstructures created by spectral density function reconstruction. It is then applied to experimental data of silicone rubbers. With the experimental data, two analyses are performed: (i) analysis of the correlation of the crack surface feature and material property and (ii) predictive model for property estimation, whereby the experiments can be potentially replaced altogether.
RESUMO
Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.