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Machine Learning Configuration Interaction.

J P Coe1

  • 1Institute of Chemical Sciences, School of Engineering and Physical Sciences , Heriot-Watt University , Edinburgh , EH14 4AS , United Kingdom.

Journal of Chemical Theory and Computation
|October 5, 2018
PubMed
Summary
This summary is machine-generated.

We introduce machine learning configuration interaction (MLCI), using neural networks to efficiently select important configurations for accurate quantum chemistry calculations. This method offers competitive accuracy and faster convergence than traditional approaches.

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Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Artificial Intelligence in Science

Background:

  • Selected configuration interaction (SCI) methods are crucial for accurate quantum chemistry but computationally demanding.
  • Identifying important electronic configurations is key to reducing the size of the configuration interaction (CI) expansion.
  • Traditional configuration selection methods can be inefficient or stochastic, impacting convergence and computational cost.

Purpose of the Study:

  • To introduce and evaluate a novel machine learning configuration interaction (MLCI) approach.
  • To leverage artificial neural networks for on-the-fly prediction of important electronic configurations.
  • To assess MLCI's efficiency and accuracy in obtaining compact wave functions for challenging molecular systems.

Main Methods:

  • An artificial neural network is trained iteratively to predict significant configurations during an SCI procedure.
  • The MLCI approach dynamically learns to distinguish important configurations not previously encountered.
  • MLCI is applied to calculate wave functions for carbon monoxide and water molecules at various geometries.

Main Results:

  • The neural network effectively discriminates between important and unimportant configurations beyond random chance.
  • MLCI successfully generates compact wave functions for carbon monoxide and addresses the multireference problem in water.
  • Compared to first-order perturbation, random, and Monte Carlo SCI, MLCI demonstrates competitive accuracy and faster convergence.

Conclusions:

  • Machine learning configuration interaction (MLCI) provides an efficient and accurate alternative for electronic structure calculations.
  • MLCI converges significantly faster than stochastic SCI methods, especially for high-accuracy computations.
  • This prototype demonstrates the potential of integrating machine learning into iterative quantum chemistry procedures.