From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics
While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher. What distinguishes research from routine execution is the progressive accumulation of knowledge - learning which approaches fail, recognizing patterns across systems, and applying understanding to new problems. However, the prevailing paradigm in AI-driven computational science treats each execution in isolation, largely discarding hard-won insights between runs. Here we present QMatSuite, an open-source platform closing this gap. Agents record findings with full provenance, retrieve knowledge before new ca...
| Authors | Haonan Huang |
|---|---|
| Primary category | physics.comp-ph |
| Published | 13 March 2026 |
| Last updated at source | 2 July 2026 |
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