A Self-Attention Ansatz for Ab-initio Quantum Chemistry
This work addresses the need for more accurate first-principles calculations in quantum chemistry and material science, representing a qualitative leap over previous methods.
The authors tackled the problem of solving the many-electron Schrödinger equation in quantum chemistry by introducing the Wavefunction Transformer (Psiformer), a self-attention-based neural network that improves ground state energy accuracy by dozens of kcal/mol on larger molecules.
We present a novel neural network architecture using self-attention, the Wavefunction Transformer (Psiformer), which can be used as an approximation (or Ansatz) for solving the many-electron Schrödinger equation, the fundamental equation for quantum chemistry and material science. This equation can be solved from first principles, requiring no external training data. In recent years, deep neural networks like the FermiNet and PauliNet have been used to significantly improve the accuracy of these first-principle calculations, but they lack an attention-like mechanism for gating interactions between electrons. Here we show that the Psiformer can be used as a drop-in replacement for these other neural networks, often dramatically improving the accuracy of the calculations. On larger molecules especially, the ground state energy can be improved by dozens of kcal/mol, a qualitative leap over previous methods. This demonstrates that self-attention networks can learn complex quantum mechanical correlations between electrons, and are a promising route to reaching unprecedented accuracy in chemical calculations on larger systems.