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Li Research Group Computational Chemistry Lab

Machine Learning

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Title	PostDate
Uncertainty quantification with graph neural networks for efficient molecular design	2025-04-05
Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks	2025-01-27
Machine learning-guided strategies for reaction conditions design and optimization	2025-01-25
Advancing vapor pressure prediction: A machine learning approach with directed message passing neural networks	2025-01-25
Advancing Climate Change Research with Machine Learning Models for Greenhouse Gas Prediction	2024-09-23
Unveiling the Role of Quantum Mechanical Descriptors in Machine Learning for Chemical Property Prediction	2024-09-23
New Approach to Explainable Uncertainty Quantification in Molecular Property Prediction	2024-09-23
Machine Learning in Chemical Kinetics and Thermochemistry	2024-09-23
Integrating Chemical Information into Reinforcement Learning for Molecular Geometry Optimization	2024-09-23
Advanced Deep Learning Model Enhances Chemical Synthesis Planning	2024-09-23
AutoTemplate—Revolutionizing Chemical Reaction Datasets for Machine Learning Applications in Organic Chemistry	2024-09-23