From Data to Theory: Autonomous Large Language Model Agents for Materials Science
This paper presents an autonomous large language model agent capable of end-to-end, data-driven materials theory development that successfully recovers established equations and proposes new relationships, while highlighting the critical need for careful validation due to the model's potential to generate mathematically fitting yet scientifically incorrect results.