Efficient machine learning configuration interaction for bond breaking problems

28 July 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Machine learning-assisted configuration interaction (MLCI) has been shown earlier as a promising method in determining the electronic structure of the model and molecular Hamiltonians. In the MLCI approach to molecular Hamiltonians, it has been noticed that prediction is strongly dependent on the connectedness of the training and validation spaces. In this work, we have tested three different models with different output parameters (abs-MLCI, transformed-MLCI, and log-MLCI) to verify the robustness of training these models. We define robustness as the extent of error in prediction even when the spaces (training and validation) are nected. We notice that the log-MLCI model is best suited to this approach and is, therefore, a powerful model for accurate one-shot variational energies. This is tested not confor chemical bond breaking in water, carbon monoxide, nitrogen, and dicarbon molecules.


configuration interaction
machine learning
strong correlation
bond breaking

Supplementary materials

Supplementary information
Supplementary information contains (1) training data from n-steps of MCCI and its effect on MLCI model building, (2) PES using CI coefficient cutoff and (3) single-point performance of MLCI models for carbon monoxide, nitrogen, and dicarbon molecules.


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