M\=oLe-{\Lambda}: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties
Researchers have developed MōLe-Λ, a novel machine learning model designed to predict quantum chemistry properties more efficiently. This model extends the existing MōLe framework to learn both the right-hand (T) and left-hand (Λ) amplitudes of the coupled-cluster (CC) response state. By jointly learning these amplitudes from localized molecular orbitals, MōLe-Λ can accurately predict energies, forces, dipoles, quadrupoles, polarizabilities, and electron densities, offering a significant speed advantage over traditional CCSD calculations. AI
