activity
20232026
most citedEmpowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression

3 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

physics.chem-ph2026

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…

cs.LG2024

Intelligent Chemical Purification Technique Based on Machine Learning

Wenchao Wu, Hao Xu, Dongxiao Zhang +1

We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purifica…

cs.LG2024

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…

cs.LG20243 cited

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…

cs.AI2023

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…