Heather J. Kulik | JP Janet

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Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design
Representations and strategies for transferable machine learning improve model performance in chemical discovery
Machine Learning in Chemistry
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 Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation
Enumerating de novo small inorganic complexes for machine learning and chemical discovery
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
Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
Predicting electronic structure properties of transition metal complexes with neural networks
Communication: Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost
Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design