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M=oLe-Lambda: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties

arXiv 2605.29622 ↗First posted 28 May 2026cs.LG physics.chem-phcited by 1

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Abstract (arXiv)

Coupled-cluster (CC) theory is often considered the gold standard of quantum chemistry, but its high computational cost limits routine access to accurate energies, forces and response properties. While the right-hand T-amplitudes determine the correlated wavefunction, many practically important observables additionally require the left-hand Lambda-amplitudes. We introduce M=oLe-Lambda, an extension of Molecular Orbital Learning (M=oLe) that predicts the full ground-state coupled-cluster singles and doubles (CCSD) response state by jointly learning right-hand amplitudes (T1,T2) and left-hand amplitudes (Lambda1,Lambda2) from localized Hartree--Fock molecular orbitals. Architecturally, M=oLe-Lambda extends M=oLe with Lambda1 and Lambda2 readouts that mirror the symmetry constraints of the T1 and T2 heads, while preserving the original equivariant orbital encoder, odd sign-equivariant decoding, locality and size-extensivity. The resulting model yields accurate CC-quality energies and forces, while simultaneously recovering dipoles, quadrupoles, polarizabilities, the electron density, and 2-electron observables such as the pair density. We show that M=oLe-Lambda further extends the speed advantage of M=oLe over full CCSD while substantially expanding the accessible properties, providing a route to wavefunction-level surrogate models for correlated quantum chemistry.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 44 references that are in the map. First posted on arXiv 28 May 2026.

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11 of 44 references.

References · 11

  1. 2022The Devil in Linear Transformercited by 98
  2. 2016By-passing the Kohn-Sham equations with machine learningcited by 65
  3. 2023Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltoniancited by 38
  4. 2023Generalizing Neural Wave Functionscited by 26
  5. 2021Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solidscited by 24
  6. 2025Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systemscited by 13
  7. 2025HIP: Hessian Interatomic Potentials without derivativescited by 11
  8. 2025Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsitycited by 10
  9. 2025High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Predictioncited by 10
  10. 2025ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitalscited by 9
  11. 2025Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Studycited by 2

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