From What to Why: A Multi-Agent System for Evidence-based Chemical
  Reaction Condition Reasoning
The chemical reaction recommendation is to select proper reaction condition
parameters for chemical reactions, which is pivotal to accelerating chemical
science. With the rapid development of large language models (LLMs), there is
growing interest in leveraging their reasoning and planning capabilities for
reaction condition recommendation. Despite their success, existing methods
rarely explain the rationale behind the recommended reaction conditions,
limiting their utility in high-stakes scientific workflows. In this work, we
propose ChemMAS, a multi-agent system that reframes condition prediction as an
evidence-based reasoning task. ChemMAS decomposes the task into mechanistic
grounding, multi-channel recall, constraint-aware agentic debate, and rationale
aggregation. Each decision is backed by interpretable justifications grounded
in chemical knowledge and retrieved precedents. Experiments show that ChemMAS
achieves 20-35% gains over domain-specific baselines and outperforms
general-purpose LLMs by 10-15% in Top-1 accuracy, while offering falsifiable,
human-trustable rationales, which establishes a new paradigm for explainable AI
in scientific discovery.
68e88b7695e8e6771df38af4
Cheng Yang
claimed_verified
2025-10-10T08:55:23.886Z
67c443afb753bd020f9c97d8
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/xbACBNLSopWmN5G1K8h_Y.png
Cheng
user
YangC777
68e88b7695e8e6771df38af5
Jiaxuan Lu
68e88b7695e8e6771df38af6
Haiyuan Wan
claimed_verified
2025-10-10T08:55:13.044Z
65a8bcb717d869bb7487c2a1
/avatars/261c28f7e616a8482970f50c1f8919fd.svg
Alex Haiyuan Wan
user
haiyuanwan
68e88b7695e8e6771df38af7
Junchi Yu
68e88b7695e8e6771df38af8
Feiwei Qin
From What to Why: A Multi-Agent System for Evidence-based Chemical
  Reaction Condition Reasoning
The chemical reaction recommendation is to select proper reaction condition
parameters for chemical reactions, which is pivotal to accelerating chemical
science. With the rapid development of large language models (LLMs), there is
growing interest in leveraging their reasoning and planning capabilities for
reaction condition recommendation. Despite their success, existing methods
rarely explain the rationale behind the recommended reaction conditions,
limiting their utility in high-stakes scientific workflows. In this work, we
propose ChemMAS, a multi-agent system that reframes condition prediction as an
evidence-based reasoning task. ChemMAS decomposes the task into mechanistic
grounding, multi-channel recall, constraint-aware agentic debate, and rationale
aggregation. Each decision is backed by interpretable justifications grounded
in chemical knowledge and retrieved precedents. Experiments show that ChemMAS
achieves 20-35% gains over domain-specific baselines and outperforms
general-purpose LLMs by 10-15% in Top-1 accuracy, while offering falsifiable,
human-trustable rationales, which establishes a new paradigm for explainable AI
in scientific discovery.
ChemMAS, a multi-agent system, improves reaction condition recommendation by providing interpretable rationales, outperforming existing methods in accuracy and explainability.
multi-agent system evidence-based reasoning mechanistic grounding multi-channel recall constraint-aware agentic debate rationale aggregation explainable AI