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Molecular Orbital Based Machine Learning for Electronic Structure
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README.md

MOB-ML

Reference implementation of Molecular Orbital Based Machine Learning (MOB-ML) for electronic structure.

Any use of this code should cite the following paper:

"A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules." L. Cheng, M. Welborn, A. S. Christensen, and T. F. Miller III, arXiv:1901.03309 (2019).

make_features.py

mobml_features.py generates MOB-ML features from the results of a HF calculation. For documentation, see the docstrings within mobml_features.py and especially the make_features function.

feature_layout.md

feature_layout.md explains the layout of the feature vectors generated by mobml_features.py.

examples

examples/ contains example inputs and outputs to mobml_features.py in Matlab MATv5 format. Each example contains inputs to the make_features function: a Fock matrix (F), Coulomb matrix (J), exchange matrix (K), dipole matrices (dipX, dipY, dipZ), and the number of core (ncore), occupied(nocc), and minimal basis orbitals (norb_min). Each example also contains reference outputs that should be generated by the make_features function: the MOB-ML features corresponding to diagonal (reference_diag_features) and off-diagonal (reference_offdiag_features) occupied LMO pairs (see Eqs. 5 and 6 of the above paper).

examples/water.mat: a single water molecule.

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