 Creating high-quality photorealistic materials for light transport simulations typically includes direct hands-on interaction with a principal shader. This means that the user has to tweak a large number of material properties by hand and has to wait for a new image of it to be rendered after each interaction. This requires a fair bit of expertise and the best setups are often obtained through a lengthy trial and error process. To enhance this workflow, we present a learning-based system for rapid mass-scale material synthesis. First, the user is presented with a gallery of materials and the assigned scores are shown in the upper left. Here, we learn the concept of glassy and transparent materials. By learning on only a few tens of high-scoring samples, our system is able to recommend many new materials from the learn distributions. The learning step typically takes a few seconds where the recommendations take negligible time and can be done on a mass scale. Then, these recommendations can be used to populate a scene with materials. Typically, each recommendation takes 40 to 60 seconds to render with global illumination, which is clearly unacceptable for real-world workflows, even for mid-size galleries. In the next step, we propose a convolutional neural network that is able to predict images of these materials that are close to the ones generated via global illumination and takes less than 3 milliseconds per image. Sometimes, a recommended material is close to the one envisioned by the user that requires a bit of fine tuning. To this end, we embed our high-dimensional shader descriptors into an intuitive 2D latent space where exploration and adjustments can take place without any domain expertise. However, this isn't very useful without additional information because the user does not know which regions offer useful material models that are in line with their scores. One of our key observations is that this latent space technique can be combined with Gaussian process regression to provide an intuitive color coding of the expected preferences to help highlighting the regions that may be of interest. Furthermore, our convolutional neural network can also provide real-time predictions of these images. These predictions are close to indistinguishable from the real-render images and are generated in real-time. Beyond the preference map, this neural network also opens up the possibility of visualizing the expected similarity of these new materials to the one we seek to fine-tune. By combining the preference and similarity maps, we obtain a color coding that guides the user in this latent space towards materials that are both similar and have a high expected score. To accentuate the utility of our real-time variant generation technique, we show a practical case where one of the great materials is almost done but requires a slight reduction in vividity. This adjustment doesn't require any domain expertise or direct interaction with the material modeling system and can be done in real-time. In this example, we learn the concept of translucent materials from only a handful of high-scoring samples and generate a large amount of recommendations from the Learn Distribution. These recommendations can then be used to populate the scene with relevant materials. Here, we show the preference and similarity maps of the Learn Translucent Material Space and explore possible variants of an input material. These recommendations can be used for mass-scale material synthesis and the amount of variation can be tweaked to suit the user's artistic vision. After assigning the appropriate materials, displacements and other advanced effects can be easily added to these materials. We have also experimented with an extended, more expressive version of our shader that also includes procedural texture del Beatles and displacements. The following scenes were populated using the Material Learning and Recommendation and latent space embedding steps. We have proposed a system for mass-scale material synthesis that is able to rapidly recommend a broad range of new material models after learning the user preferences from a modest number of samples. Beyond this pipeline, we also explored powerful combinations of the three use-learning algorithms, thereby opening up the possibility of real-time photorealistic material visualization, exploration and fine-tuning in a 2D latent space. We believe this feature set offers a useful solution for rapid mass-scale material synthesis for novice and expert users alike and hope to see more exploratory works combining the advantages of multiple state-of-the-art learning algorithms in the future.