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Warm pressing of organosilicon polymers is often challenging due to the formation of cracks due to release of volatile compounds during the warm pressing process. The focus of the present study is to warm press crosslinked SMP‐10 powders into crack‐free compacts and to pyrolyze them to get bulk SiC monoliths. Crack formation during warm pressing is addressed by optimizing the crosslinking temperature, and the loss of formability of the powders crosslinked at higher temperatures is overcome with the use of uncured polymer as a binder. The crosslinking temperature of the preceramic polymer plays a crucial role in developing crack‐free green bodies. The amount of binder used is varied to study its effect on the bulk density of the pyrolyzed product. The warm pressed green bodies pyrolyzed at 1400 °C result in the formation of bulk silicon carbide ceramics and are characterized using X‐ray diffractometer and FTIR spectroscopy. Warm pressing is performed at a lower temperature than reported in the literature, and this limits the incorporation of oxygen during the warm pressing. 

Predicting materials’ microstructure from the desired properties is critical for exploring new materials. Herein, a novel regression‐based prediction of scanning electron microscopy (SEM) images for the target hardness using generative adversarial networks (GANs) is demonstrated. This article aims at generating realistic SEM micrographs, which contain rich features (e.g., grain and neck shapes, tortuosity, spatial configurations of grain/pores). Together, these features affect material properties but are difficult to predict. A high‐performance GAN, named ‘Microstructure‐GAN’ (or M‐GAN), with residual blocks to significantly improve the details of synthesized micrographs is established . This algorithm was trained with experimentally obtained SEM micrographs of laser‐sintered alumina. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed even at the nanometer scale (∼50 nm) in the predicted 1000× micrographs. A pretrained convolutional neural network (CNN) was used to evaluate the accuracy of the predicted micrographs with rich features for specific hardness. The relative bias of the CNN‐evaluated value of the generated micrographs was within 2.1%–2.7% from the values for experimental micrographs. This approach can potentially be applied to other microscopy data, such as atomic force, optical, and transmission electron microscopy.