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Chenru Duan

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design
Representations and strategies for transferable machine learning improve model performance in chemical discovery
Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
Machine-learning assisted workflows for inorganic molecular discovery
Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation
Hybrid machine-learning and first-principles design for transition metal complexes
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

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