Efficient Machine Learning Configuration Interaction for Bond Breaking Problems

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 not connected. 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 for chemical bond breaking in water, carbon monoxide, nitrogen, and dicarbon molecules.