论文标题
更好，更快的费米子神经网络
Better, Faster Fermionic Neural Networks
论文作者
论文摘要
费米子神经网络（Ferminet）是一种最近开发的神经网络结构，可以用作多电子系统的波函数ANSATZ，并且已经在小型系统上表现出很高的精度。在这里，我们对费梅内特（Ferminet）进行了一些改进，使我们能够在具有挑战性的系统上为速度和准确性设置新的记录。我们发现，增加网络的大小足以达到像氩一样大的原子上的化学精度。通过在JAX中实现费米特并简化网络的几个部分的结合，我们能够减少大型系统上训练费米内特所需的GPU小时数量。这使我们能够对Bicyclobutane的挑战性过渡到丁二烯和与Paulinet进行挑战性过渡，并与CyclobutaDiene的自动化进行比较，并且我们在两者的最新状态下取得了成果。
The Fermionic Neural Network (FermiNet) is a recently-developed neural network architecture that can be used as a wavefunction Ansatz for many-electron systems, and has already demonstrated high accuracy on small systems. Here we present several improvements to the FermiNet that allow us to set new records for speed and accuracy on challenging systems. We find that increasing the size of the network is sufficient to reach chemical accuracy on atoms as large as argon. Through a combination of implementing FermiNet in JAX and simplifying several parts of the network, we are able to reduce the number of GPU hours needed to train the FermiNet on large systems by an order of magnitude. This enables us to run the FermiNet on the challenging transition of bicyclobutane to butadiene and compare against the PauliNet on the automerization of cyclobutadiene, and we achieve results near the state of the art for both.
