3 citations · 5 across the 4 of their papers we have counts for
5 papers
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…
A Cross-Domain Graph Learning Protocol for Single-Step Molecular Geometry Refinement
Chengchun Liu, Wendi Cai, Boxuan Zhao +1
Accurate molecular geometries are a prerequisite for reliable quantum-chemical predictions, yet density functional theory (DFT) optimization remains a major bottleneck for high-thr…
Infrared Spectra Prediction for Diazo Groups Utilizing a Machine Learning Approach with Structural Attention Mechanism
Chengchun Liu, Fanyang Mo
Infrared (IR) spectroscopy is a pivotal technique in chemical research for elucidating molecular structures and dynamics through vibrational and rotational transitions. However, th…
Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression
Siyu Lou, Chengchun Liu, Yuntian Chen +1
Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those d…
Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence
Chengchun Liu, Yuntian Chen, Fanyang Mo
Organic chemistry is undergoing a major paradigm shift, moving from a labor-intensive approach to a new era dominated by automation and artificial intelligence (AI). This transform…