Material Discovery

Material discovery research aims to accelerate the identification and design of novel materials with desired properties, reducing the time and cost associated with traditional methods. Current efforts leverage machine learning, employing diverse models such as graph neural networks (GNNs), large language models (LLMs), and generative flow networks (GFNs), often integrated with Bayesian optimization and active learning strategies to efficiently explore vast chemical and structural spaces. These advancements promise to significantly impact various fields by enabling the rapid development of advanced materials for applications in energy, electronics, and beyond.