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SEAMM MBE Plug-in

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A SEAMM plug-in for many-body expansion (MBE) corrections of periodic cells and clusters

Features

  • Please edit this section!

Acknowledgements

This package was created with the molssi-seamm/cookiecutter-seamm-plugin tool, which is based on the excellent Cookiecutter.

Developed by the Molecular Sciences Software Institute (MolSSI), which receives funding from the National Science Foundation under award CHE-2136142.

History

2026.10.4 – Requires seamm-exec 2026.10.3.2 for running on a cluster
  • Requires seamm-exec 2026.10.3.2: each bundle of calculations sent to a cluster’s queue now runs on one node, a calculation runs inside the job itself only when it fits there, and bundles get a time limit from their calculations’ estimated cost. Before, a VASP or ORCA calculation could be spread over several nodes and fail, or be cut off by the queue’s default time limit.

  • Checked end to end on TinkerCliffs: the 64-water pilot frame reproduces the prototype’s labels (energy to 0.001 meV, forces to 0.2 meV/Å RMS, pressures to about 1 atm).

2026.10.3 – Initial release: many-body expansion (MBE) corrections
  • A step that estimates high-level energies, forces and, for periodic cells, the stress of a cell or a large cluster: a low-level calculation of the whole system plus [high - low] corrections computed on monomers, pairs and triples of molecules, using the seamm_mbe library.

  • The levels are model chemistries: the high level, a molecular low level, and optionally a periodic low level (e.g. VASP) for the fragments of periodic cells, run as batches of calculations through seamm_exec.

  • Pair and triple cutoffs, a rule for which triples to include, and a check that the cell is large enough for the chosen cutoffs.

  • For a periodic low level, the fragments are placed on the cell’s FFT grid so that the grid errors cancel between the cell and its fragments.

  • Optional pairwise counterpoise correction of the basis-set superposition error.

  • Optional energy offsets per molecule type, and the results stored as properties and written to an extended XYZ file for training machine-learned force fields.

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