AutoMat benchmark shows current LLM coding agents achieve at most 54.1% success when reproducing computational materials science claims from papers.
arXiv preprint arXiv:2505.10852 , year=
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MatClaw is a code-first LLM agent that autonomously executes end-to-end materials workflows by generating and running Python scripts on remote clusters, achieving reliable code generation via memory architecture and RAG while requiring guided interventions for tacit knowledge.
citing papers explorer
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Can Coding Agents Reproduce Findings in Computational Materials Science?
AutoMat benchmark shows current LLM coding agents achieve at most 54.1% success when reproducing computational materials science claims from papers.
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MatClaw: An Autonomous Code-First LLM Agent for End-to-End Materials Exploration
MatClaw is a code-first LLM agent that autonomously executes end-to-end materials workflows by generating and running Python scripts on remote clusters, achieving reliable code generation via memory architecture and RAG while requiring guided interventions for tacit knowledge.