Exciting progress from Daniel King, whose work with the Gagliardi Group and collaborators has led to the development of CEONet, a new AI method that predicts properties of quantum orbitals with unprecedented speed and physical intuition.
By building physics directly into the model, Daniel has tackled one of the core challenges of orbital analysis—the parity problem—opening the door to faster, more automated interpretation of electronic structure and accelerating advanced quantum chemistry methods.
A major step forward for computational chemistry and a great example of Daniel’s innovative approach to bridging AI and quantum science.
Read the article: New AI Method Predicts Properties of Quantum Orbitals with Intuitive Speed