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Efficient Machine Learning Configuration Interaction for Bond Breaking Problems.

Madhumita Rano1, Debashree Ghosh1

  • 1School of Chemical Sciences, Indian Association for the Cultivation of Science, 2A & 2B, Raja Subodh Chandra Mallick Road, Jadavpur, Kolkata 700032, India.

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|April 16, 2023
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Summary
This summary is machine-generated.

Machine learning-assisted configuration interaction (MLCI) models were tested for robustness. The log-MLCI model demonstrated superior accuracy in predicting electronic structures, even with disconnected training and validation spaces.

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Area of Science:

  • Computational chemistry
  • Quantum chemistry
  • Materials science

Background:

  • Machine learning-assisted configuration interaction (MLCI) is a promising technique for electronic structure determination.
  • MLCI's predictive accuracy is sensitive to the connectivity between training and validation datasets.
  • Robustness in MLCI is crucial for reliable predictions, especially when training and validation spaces are disconnected.

Purpose of the Study:

  • To evaluate the robustness of different MLCI models under disconnected training and validation spaces.
  • To identify the most reliable MLCI model for accurate one-shot variational energies.
  • To assess the performance of MLCI models in simulating chemical bond breaking.

Main Methods:

  • Tested three MLCI models: abs-MLCI, transformed-MLCI, and log-MLCI.
  • Defined and quantified robustness based on prediction error with disconnected spaces.
  • Applied models to simulate chemical bond breaking in H2O, CO, N2, and C2 molecules.

Main Results:

  • The log-MLCI model exhibited the highest robustness and accuracy.
  • Prediction errors were minimized with the log-MLCI approach, even with unconnected spaces.
  • All tested models showed varying degrees of success in simulating bond breaking.

Conclusions:

  • The log-MLCI model is the most suitable for robust and accurate one-shot variational energy calculations.
  • This approach holds significant potential for electronic structure calculations in various molecular systems.
  • Further development of MLCI methods can enhance their applicability in computational chemistry.