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Transferable neural wavefunctions for solids.

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  • 1Faculty of Mathematics, University of Vienna, Vienna, Austria.

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This summary is machine-generated.

Deep learning accelerates quantum chemistry calculations by optimizing a single neural network across multiple solid-state systems. This approach significantly reduces computational cost for simulating materials.

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Area of Science:

  • Quantum Chemistry
  • Computational Materials Science
  • Artificial Intelligence in Science

Background:

  • Deep-learning-based variational Monte Carlo (DL-VMC) offers high accuracy for the many-electron Schrödinger equation.
  • DL-VMC methods face challenges due to the high computational cost of optimizing neural network weights for each new system.

Purpose of the Study:

  • To extend the approach of optimizing a single neural network across multiple systems to solid-state materials.
  • To reduce the computational cost associated with simulating solids, which involve diverse geometries and conditions.

Main Methods:

  • Implemented a single neural network ansatz optimized across various geometries, boundary conditions, and supercell sizes for solid-state calculations.
  • Transferred a pre-trained neural network from smaller (2x2x2) to larger (3x3x3) LiH supercells.

Main Results:

  • Optimization of a single ansatz across different solid-state variations significantly reduced the number of optimization steps required.
  • Simulating larger 3x3x3 LiH supercells using a transferred network required 50 times fewer optimization steps compared to previous methods.

Conclusions:

  • Optimizing a single neural network across multiple solid-state systems is a viable strategy to decrease computational overhead.
  • This transfer learning approach shows promise for efficient high-throughput materials simulations.