MōLe-Λ: AI model accelerates quantum chemistry calculations

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

RANK_REASON The cluster contains a research paper detailing a new machine learning model for quantum chemistry calculations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Coupled-cluster
• Machine learning
• MōLe-Λ
• Quantum chemistry

• paper
• model release

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