论文标题
深层蒙特卡洛的电子激发态
Electronic excited states in deep variational Monte Carlo
论文作者
论文摘要
在多种重要应用中，获得电子系统的准确地面和低洼激发态至关重要。解决大型系统范围缩放的Schrödinger方程的一种从头算法是变异量子蒙特卡洛（QMC）。最近引入的深QMC方法使用以深神经网络代表的Ansatzes，并为包含多达几十个电子的分子生成了几乎精确的地面溶液，并有可能扩展到更大的系统，而其他高度准确的方法不可行。在本文中，我们将这样的Ansatz（Paulinet）扩展到计算电子激发态。我们在各种小原子和分子上演示了我们的方法，并始终如一地实现低洼状态的高精度。为了强调该方法的潜力，我们计算了较大的苯分子的第一个激发态，以及乙烯的圆锥形交集，以及Paulinet匹配更昂贵的高级方法的结果。
Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications. One ab initio method for solving the Schrödinger equation that scales favorably for large systems is variational quantum Monte Carlo (QMC). The recently introduced deep QMC approach uses ansatzes represented by deep neural networks and generates nearly exact ground-state solutions for molecules containing up to a few dozen electrons, with the potential to scale to much larger systems where other highly accurate methods are not feasible. In this paper, we extend one such ansatz (PauliNet) to compute electronic excited states. We demonstrate our method on various small atoms and molecules and consistently achieve high accuracy for low-lying states. To highlight the method's potential, we compute the first excited state of the much larger benzene molecule, as well as the conical intersection of ethylene, with PauliNet matching results of more expensive high-level methods.
