The combination of neural networks and quantum Monte Carlo methods has arisen as a path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers with an antisymmetric layer to satisfy the antisymmetry requirements of the electronic wavefunction. However, to date it is unclear if one can represent antisymmetric functions of physical interest, and it is difficult to measure the expressiveness of the antisymmetric layer. This work attempts to address this problem by introducing explicitly antisymmetrized universal neural network layers as a diagnostic tool. We first introduce a generic antisymmetric (GA) layer, which we use to replace the entire antisymmetric layer of the highly accurate ansatz known as the FermiNet. We demonstrate that the resulting FermiNet-GA architecture can yield effectively the exact ground state energy for small systems. We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing products of determinants with products of antisymmetrized neural networks. Interestingly, the resulting FermiNet-FA architecture does not outperform the FermiNet. This suggests that the sum of products of antisymmetries is a key limiting aspect of the FermiNet architecture. To explore this further, we investigate a slight modification of the FermiNet called the full determinant mode, which replaces each product of determinants with a single combined determinant. The full single-determinant FermiNet closes a large part of the gap between the standard single-determinant FermiNet and FermiNet-GA. Surprisingly, on the nitrogen molecule at a dissociating bond length of 4.0 Bohr, the full single-determinant FermiNet can significantly outperform the standard 64-determinant FermiNet, yielding an energy within 0.4 kcal/mol of the best available computational benchmark.
Beyond single-reference fixed-node approximation in ab initio Diffusion Monte Carlo using antisymmetrized geminal power applied to systems with hundreds of electrons
Basis-free neural-network geminal and Jastrow factors for variational Monte Carlo
University of Paderborn（帕德博恩大学）；Center for Advanced Systems Understanding (CASUS)（先进系统理解中心）；Helmholtz Zentrum Dresden-Rossendorf（德累斯顿-罗斯多夫亥姆霍兹中心）；Technische Universität Dresden（德累斯顿工业大学）
Convergence of variational Monte Carlo simulation and scale-invariant pre-training
The Simons Institute for the Theory of Computing（西蒙斯理论计算研究所）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Variational and Diffusion Quantum Monte Carlo Calculations with the CASINO Code
University of Cambridge（剑桥大学）；University College London（伦敦大学学院）；Lancaster University（兰开斯特大学）；Max Planck Institute for Solid State Research（马克斯·普朗克固体研究所）
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
University of California, Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）；St. Kliment Ohridski University of Sofia（索非亚圣克里门特·奥赫里德大学）；Max Planck Institute for the Physics of Complex Systems（马克斯·普朗克复杂系统物理研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
mVMC - Open-source software for many-variable variational Monte Carlo method
mVMC（many-variable Variational Monte Carlo）是一个基于变分蒙特卡罗方法的开源软件，适用于相互作用费米子系统的各种哈密顿量。在mVMC中，我们引入了超过一万个变参数，并同时优化他们使用随机重构（SR）方法。在本文中，我们解释mVMC的基础知识和用户界面。通过使用mVMC，用户可以通过为广泛研究的量子晶格模型准备大约十行的一个输入文件来执行计算，并且还可以通过准备几个额外的输入文件来执行一般的哈密顿量。我们展示了哈伯德模型、海森堡模型和近藤晶格模型的mVMC的基准结果。这些基准结果表明，mVMC提供的基态和低能激发态波函数相互作用费米子系统的高精度。
Resonating valence bond pairing energy in graphene by quantum Monte Carlo
University of Manchester（曼彻斯特大学）；Center for Advanced Systems Understanding（先进系统理解中心）；Helmholtz Zentrum Dresden-Rossendorf（德累斯顿-罗斯多夫亥姆霍兹中心）；TU Dresden（德累斯顿工业大学）
