Agentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications
Autoresearch offers a flexible paradigm for automating scientific tasks, in which an AI agent proposes, implements, evaluates, and refines candidate solutions against a quantitative objective. Here, we use composition-based materials-property prediction to test whether such agents can perform a task beyond model selection and hyperparameter optimization: the design of input descriptors. We introduce Automat, an autoresearch framework where a coding agent based on a large language model generates composition-only descriptors for chemical compounds and evaluates them using a random forest workflow. The agent is restricted to information derivable from chemical formulas and iteratively propo...
| Authors | Matteo Cobelli, Stefano Sanvito |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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