The utilization of machine learning in Materials Science underscores the critical importance of the quality and quantity of data in training models effectively. Unlike fields such as image processing and natural language processing, there is limited availability of atomistic datasets, leading to biases in training data. Particularly in the domain of materials discovery, there exists an issue of continuity in atomistic datasets. Experimental data sourced from literature and patents is usually only available for favorable data, resulting in bias in the training dataset. This study focuses on developing a SMILES-based model for generating synthetic datasets of quantum materials using a variational autoencoder. This study centers on the generation of a synthetic dataset of quantum materials specifically for quantum sensing applications, with a focus on two-level quantum molecules that exhibit a dipole blockade. The proposed technique offers an improved sampling algorithm by incorporating newly generated data into the sampling algorithm to create a more normally distributed dataset. Through this technique, the study was able to generate over 1 000 000 candidate quantum materials from a small dataset of only 8000 materials. The generated dataset identified several iodine-containing molecules as promising single photon emitting materials for potential quantum sensing applications.