MatGym: An Agent-Centric Environment Bridging Materials Discovery and Reproducible Synthesis

Materials discovery requires exploring how composition and processing affect performance when these relationships are only partly known. We introduce MatGym, an agent-centric environment for evaluating autonomous exploration and reproducible delivery across five simulated materials laboratories. Given a material specification and a resource budget, agents conduct open-ended exploration: they select candidates, control preparation steps, and choose computations and measurements to inform subsequent actions. Each task ends with a sample and a recipe, evaluated for service performance, agreement with the sample's preparation history, and replication on fresh batches. Across 52 tasks using the largest implemented candidate libraries, with three attempts per task, Codex and Claude Code achieve success rates of 43.6% and 41.0%, respectively. Similar overall rates mask different strengths across laboratories, and each system solves fewer than 30% of tasks successfully in all three attempts. Separate delivery checks distinguish material performance, process traceability, and batch reproducibility. In auxiliary transfer tests with an earlier Codex configuration, success on new ALD tasks increases from 30.6% without memory to 55.6% with prior campaign memory, while gains across material systems vary. MatGym provides a controlled setting for evaluating how agents explore unfamiliar material processes, deliver reproducible outcomes, and reuse experience on new tasks.