Deep neural networks have become a highly accurate and powerful wavefunction ansatz in combination with variational Monte Carlo methods for solving the electronic Schr\"odinger equation. However, despite their success and favorable scaling, these methods are still computationally too costly for wide adoption. A significant obstacle is the requirement to optimize the wavefunction from scratch for each new system, thus requiring long optimization. In this work, we propose a novel neural network ansatz, which effectively maps uncorrelated, computationally cheap Hartree-Fock orbitals, to correlated, high-accuracy neural network orbitals. This ansatz is inherently capable of learning a single wavefunction across multiple compounds and geometries, as we demonstrate by successfully transferring a wavefunction model pre-trained on smaller fragments to larger compounds. Furthermore, we provide ample experimental evidence to support the idea that extensive pre-training of a such a generalized wavefunction model across different compounds and geometries could lead to a foundation wavefunction model. Such a model could yield high-accuracy ab-initio energies using only minimal computational effort for fine-tuning and evaluation of observables.
翻译:深度神经网络已经成为解决电子薛定谔方程的变分蒙特卡罗方法中高精度且强大的波函数自然参数。然而,尽管这些方法的成功和良好的可扩展性,它们在计算上仍然太昂贵难以普及。其中一个重要障碍是对于每个新体系需要从零开始优化波函数,这需要很长时间的优化。在这项工作中,我们提出了一种新颖的神经网络自然参数,该方法有效地将不相关的、计算成本低廉的哈特里·福克轨道映射到相关的、高精度的神经网络轨道中。我们通过成功将预训练的小分子波函数模型转移到大分子中来证明该自然参数能够学习单个在多个化合物和几何形态下的波函数。此外,我们提供了充分的实验证据支持这样一个概念,即在不同的化合物和结构上广泛预训练的泛化波函数模型可以产生基础波函数模型。这样的模型能够使用最小的计算量对参数进行调整和计算协同变量,从而获得高精度的从头算能量。