arXiv — Machine Learning · · 3 min read

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

Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.

Computer Science > Machine Learning

arXiv:2605.29622 (cs)
[Submitted on 28 May 2026]

Title:MōLe-Λ: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties

View a PDF of the paper titled M\=oLe-{\Lambda}: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties, by Andreas Burger and 6 other authors
View PDF HTML (experimental)
Abstract: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 MōLe-$\Lambda$, an extension of Molecular Orbital Learning (MōLe) that predicts the full ground-state coupled-cluster singles and doubles (CCSD) response state by jointly learning right-hand amplitudes $(T_1,T_2)$ and left-hand amplitudes $(\Lambda_1,\Lambda_2)$ from localized Hartree--Fock molecular orbitals. Architecturally, MōLe-$\Lambda$ extends MōLe with $\Lambda_1$ and $\Lambda_2$ readouts that mirror the symmetry constraints of the $T_1$ and $T_2$ 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 MōLe-$\Lambda$ further extends the speed advantage of MōLe over full CCSD while substantially expanding the accessible properties, providing a route to wavefunction-level surrogate models for correlated quantum chemistry.
Comments: ICML 2026 AI4Physics
Subjects: Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
Cite as: arXiv:2605.29622 [cs.LG]
  (or arXiv:2605.29622v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.29622
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Andreas Burger [view email]
[v1] Thu, 28 May 2026 08:56:22 UTC (4,699 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled M\=oLe-{\Lambda}: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties, by Andreas Burger and 6 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source

Current browse context:

cs.LG
< prev   |   next >
Change to browse by:

References & Citations

Loading...

BibTeX formatted citation

loading...
Data provided by:

Bookmark

BibSonomy Reddit
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from arXiv — Machine Learning