1 citations · 1 across the 4 of their papers we have counts for
6 papers
Explainable Molecular Structure Inference from GC--MS with Diffusion Models and LLM Reranking
Changlin Liu, Tianyu Yi, Chengchun Liu +2
GC--EI--MS is an important technique for analyzing volatile and semivolatile compounds in complex samples. However, conventional methods rely heavily on reference spectral library…
Divergence Decoding: Training-Free Capability Fusion
Yimi Wang, Hao Li, Shuo Yang +6
While large language models excel in reasoning, these generalists often lack knowledge for specialized scientific domains. Conversely, domain models~(specialists), while knowledgea…
Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data
Chengchun Liu, Zhiyuan Yan, Li Yuan +5
Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are spar…
MoleCode unlocks structural intelligence in large language models
Zhiyuan Yan, Chen Liu, Boxuan Zhao +8
Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular representation, SMILES, compresses…
Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval
Mengjia Niu, Hao Li, Jie Shi +2
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, although their susceptibility to hallucination poses significant challenges for their…
Deep peak property learning for efficient chiral molecules ECD spectra prediction
Hao Li, Da Long, Li Yuan +3
Chiral molecule assignation is crucial for asymmetric catalysis, functional materials, and the drug industry. The conventional approach requires theoretical calculations of electro…