A Self-Attention Ansatz for Ab-initio Quantum Chemistry

要約

タイトル：自己注意アプローチによる第一原理量子化学のAnsatz（近似手法）について
要約：
– 第一原理量子計算において、Deep Neural networkがFermiNetやPauliNetといった以前の手法に比べて高い精度を誇る一方で、電子間の相互作用を制御するアテンション機構を欠いていた。
– そこで、本研究グループが開発したWavefunction Transformer（Psiformer）は、自己注意メカニズムを用い、従来の手法の代替手段として、より高い精度で量子計算を行うことができる。
– 特に大型の分子に対しては以前の手法と比べて極めて高い精度を誇ることができ、化学計算において新たな可能性を示唆するものである。

要約(オリジナル)

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 Schr\’odinger 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.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI