MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
Constructing production-quality machine-learned interatomic potentials (MLIPs) requires balancing accuracy, dynamical stability, and computational throughput under constraints that are not captured by a single training loss. We introduce MLIPilot, an auto-research framework in which tool-calling large language models propose hypotheses, edit MLIP training code, launch HPC jobs, and accept or revert changes using a fixed, physically constrained scorecard. We evaluate MLIPilot on MACE potential optimization using both commercial and open-weight LLM agents, including GPT-5.5, GPT-4.1, Mistral-24B, and Qwen3-32B. The benchmarks span molecular and periodic settings: a QM7-derived dataset for w...
| Authors | Etinosa Osaro, Santosh Adhikari, Stamatia Zavitsanou et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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