We propose the concept of machine learning configuration interaction (MLCI) whereby an artificial neural network is trained on-the-fly to predict important new configurations in an iterative selected configuration interaction procedure. We demonstrate that the neural network can discriminate between important and unimportant configurations, that it has not been trained on, much better than by chance. MLCI is then used to find compact wave functions for carbon monoxide at both stretched and equilibrium geometries. We also consider the multireference problem of the water molecule with elongated bonds. Results are contrasted with those from other ways of selecting configurations: first-order perturbation, random selection, and Monte Carlo configuration interaction. Compared with these other serial calculations, this prototype MLCI is competitive in its accuracy, converges in significantly fewer iterations than the stochastic approaches, and requires less time for the higher-accuracy computations.

中文翻译：

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机器学习配置交互

我们提出了机器学习配置交互（MLCI）的概念，其中在训练过程中人工神经网络进行动态训练，以预测迭代选择的配置交互过程中的重要新配置。我们证明了神经网络可以区分重要的和不重要的配置（尚未经过训练），这比偶然情况要好得多。然后，将MLCI用于在拉伸和平衡几何形状下找到一氧化碳的紧波函数。我们还考虑了带有延长键的水分子的多参考问题。结果与其他选择配置方式的结果形成对比：一阶扰动，随机选择和蒙特卡洛配置相互作用。与这些其他串行计算相比，