Synthesizing or studying certain materials in a laboratory setting often poses challenges due to safety concerns, impractical experimental conditions, or cost constraints. In response, scientists are increasingly turning to deep learning methods that involve developing and training machine learning models to recognize patterns and relationships in data that include information about material properties, compositions, and behaviors.