fig2
From: Agentic material science
Figure 2. Overview of CheMatAgent for knowledge question answering[33]. Agents can obtain more professional knowledge and generate more accurate answers by using professional tools for information retrieval and prediction. Reproduced from “CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning”, arXiv:2506.07551, under CC BY 4.0 license[33]. LLMs: Large language models; CAS: Chemical Abstracts Service; TPSA: topological polar surface area; QED: quantitative estimate of drug-likeness; LoRA: low-rank adaptation; RRM: reasoning reward model; ORM: outcome reward model.
