Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models
Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations. Learning the mechanism logic is therefore essential for enhancing the fundamental chemical intelligence of large language models (LLMs). The stepwise deduction of reaction mechanism aligns naturally with the reasoning paradigms of reasoning LLMs. However, current chemical LLMs primarily emphasize coarse-grained name reactions for product prediction and retrosynthesis, often leading to physical inconsistencies and hallucinations. In contrast, specialized small-scale generativ
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- PossiblePossibly related (embedding) · 50%sileod/reasoning-core →
- PossiblePossibly related (embedding) · 49%benjaminzwhite/reasoning-models →
- PossiblePossibly related (embedding) · 48%dralgroup/mlatom →
- PossiblePossibly related (embedding) · 47%amitshekhariitbhu/llm-internals →
- LinkedLinked via arxiv author · 85%Xingyu Dang →
“Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models”
- LinkedLinked via arxiv author · 85%Haocheng Tang →
“Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models”
- LinkedLinked via arxiv author · 85%Junmei Wang →
“Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models”
- LinkedLinked via arxiv author · 85%Yanjun Li →
“Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models”