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KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory.

R Remme1, T Kaczun1, M Scheurer1

  • 1IWR, Heidelberg University Im Neuenheimer Feld 205, 69120 Heidelberg Baden-Württemberg, Germany.

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|October 13, 2023
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Summary

Researchers developed KineticNet, an AI model that learns the kinetic energy functional for orbital-free density functional theory (OF-DFT). This breakthrough enables accurate computation of molecular properties, advancing computational chemistry.

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Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Materials Science

Background:

  • Orbital-free density functional theory (OF-DFT) offers a computationally inexpensive method for calculating molecular properties.
  • A major limitation of OF-DFT is the difficulty in accurately determining the kinetic energy as a direct functional of the electron density.
  • Existing methods struggle with the expressivity, spatial context, and data requirements for learning accurate kinetic energy functionals.

Purpose of the Study:

  • To develop a novel deep learning approach for learning the kinetic energy functional in OF-DFT.
  • To address the challenges of model expressivity, spatial context, and training data generation for accurate kinetic energy functional prediction.
  • To achieve chemical accuracy in predicting molecular properties using the learned kinetic energy functional.

Main Methods:

  • Introduced KineticNet, an equivariant deep neural network utilizing point convolutions for predicting quantities on molecular quadrature grids.
  • Designed convolution filters with high spatial resolution near nuclear cusps and an atom-centric architecture for information propagation.
  • Developed a data generation strategy involving random external potential perturbations to create diverse training datasets.

Main Results:

  • KineticNet successfully learned kinetic energy functionals with chemical accuracy for various input densities and geometries of small molecules.
  • Demonstrated the capability of OF-DFT density optimization with chemical accuracy for two-electron systems using the learned functionals.
  • Achieved efficient computation on GPUs due to a limited memory footprint.

Conclusions:

  • KineticNet represents a significant advancement in OF-DFT, enabling accurate kinetic energy functional prediction.
  • The developed AI model overcomes key limitations in OF-DFT, paving the way for more efficient and accurate computational chemistry.
  • This work demonstrates the potential of deep learning to solve fundamental challenges in electronic structure theory.