1 citations · 2 across the 2 of their papers we have counts for
5 papers
Chem-R: Learning to Reason as a Chemist
Weida Wang, Benteng Chen, Di Zhang +14
Although large language models (LLMs) have significant potential to advance chemical discovery, current LLMs lack core chemical knowledge, produce unreliable reasoning trajectories…
SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
Zhuo Yang, Jiaqing Xie, Shuaike Shen +13
Deep learning holds immense promise for spectroscopy, yet research and evaluation in this emerging field often lack standardized formulations. To address this issue, we introduce S…
SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning
Shuaike Shen, Jiaqing Xie, Zhuo Yang +6
Large language models have emerged as transformative tools in molecular science, demonstrating remarkable potential in molecular property prediction and de novo molecular design. H…
ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System
Dong Han, Zhehong Ai, Pengxiang Cai +16
Bayesian optimization (BO) is a powerful tool for scientific discovery in chemistry, yet its efficiency is often hampered by the sparse experimental data and vast search space. Her…
Chem3DLLM: 3D Multimodal Large Language Models for Chemistry
Lei Jiang, Shuzhou Sun, Biqing Qi +5
In the real world, a molecule is a 3D geometric structure. Compared to 1D SMILES sequences and 2D molecular graphs, 3D molecules represent the most informative molecular modality.…