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arXiv 2203.09472physics.chem-phphysics.comp-phstat.ML
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电子激发态的深变分蒙特卡罗模拟

Electronic excited states in deep variational Monte Carlo

Mike Entwistle, Zeno Schätzle, Paolo A. Erdman, Jan Hermann, Frank Noé

摘要:

获得电子系统的精确基态和低激发态激发态在许多重要应用中是至关重要的。一种求解薛定谔方程的从头算方法是变分量子蒙特卡罗(QMC)。最近引入的深度QMC方法使用由深度神经网络表示的ansatzes,并为包含多达几十个电子的分子生成几乎精确的基态解,有可能扩展到其他高精度方法不可行的更大系统。在本文中,我们推广了一个这样的模型(PauliNet)来计算电子激发态。我们在各种小原子和分子上演示了我们的方法,并始终实现了低能级的高精度。为了突出该方法的潜力,我们计算的第一个激发态的大得多的苯分子,以及乙烯的圆锥形交叉点,与PauliNet匹配结果更昂贵的高层次的方法。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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