1 citations · 2 across the 5 of their papers we have counts for
6 papers
Where Should Physics Enter a Molecular Crystal Generator?
Haocheng Tang, Junmei Wang, Wengong Jin
Generative models make molecular crystal structure prediction fast, but their samples still exhibit geometric and packing violations. Physics can be introduced during training, pos…
Localize, Then Reason: Visual Latent Structural Reasoning for Molecular Properties and Edits
Xingqiao Lin, Junmei Wang, Haocheng Tang
Local chemical perception and property reasoning are both essential for understanding how molecular structure determines properties. Current LLM-based chemical reasoning methods ei…
Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models
Xingyu Dang, Haocheng Tang, Junmei Wang +1
Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations. Learning the mechanism logic is therefore essential for enh…
Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics
Haocheng Tang, Liang Shi, Ya-Shi Zhang +3
Protein dynamics underlie many biological functions, yet remain difficult to characterize due to the high computational cost of molecular dynamics simulations and the scarcity of d…
Fine-tuning DeepSeek-OCR-2 for Molecular Structure Recognition
Haocheng Tang, Xingyu Dang, Junmei Wang
Optical Chemical Structure Recognition (OCSR) is critical for converting 2D molecular diagrams from printed literature into machine-readable formats. While Vision-Language Models h…
Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule Generation
Haocheng Tang, Jing Long, Beihong Ji +1
In this work, we introduce Auxiliary Discriminator Sequence Generative Adversarial Networks (ADSeqGAN), a novel approach for molecular generation in small-sample datasets. Traditio…