论文标题
通过生成机器学习，在配置空间中求解Schrödinger方程
Solving the Schrödinger Equation in the Configuration Space with Generative Machine Learning
论文作者
论文摘要
配置互动方法提供了一种概念上简单而有力的方法来解决schrödinger方程的现实分子和材料，但其特征是不利的缩放，这极大地限制了其实际适用性。有效地仅选择实际上有助于波功能的配置是迈向实际应用的基本步骤。我们提出了一种机器学习方法，该方法迭代地训练生成模型以优先生成重要的配置。通过考虑分子应用，可以表明，相对于随机采样或蒙特卡洛构型相互作用方法，可以更快地达到化学精度。这项工作为更广泛的生成模型铺平了道路，以解决电子结构问题。
The configuration interaction approach provides a conceptually simple and powerful approach to solve the Schrödinger equation for realistic molecules and materials but is characterized by an unfavourable scaling, which strongly limits its practical applicability. Effectively selecting only the configurations that actually contribute to the wavefunction is a fundamental step towards practical applications. We propose a machine learning approach that iteratively trains a generative model to preferentially generate the important configurations. By considering molecular applications it is shown that convergence to chemical accuracy can be achieved much more rapidly with respect to random sampling or the Monte Carlo configuration interaction method. This work paves the way to a broader use of generative models to solve the electronic structure problem.
