论文标题
使用机器学习算法自动选择用于强相关系统的活动空间
Automatic selection of active spaces for strongly correlated systems using machine learning algorithms
论文作者
论文摘要
主动空间量子化学方法可以提供非常准确的描述密切相关的电子系统，这对于自然科学具有巨大的价值。活动空间的正确选择至关重要，但是一项非平凡的任务。在本文中，我们介绍了基于神经网络（NN）的方法，用于自动选择主动空间，该空间侧重于过渡金属系统。训练集是由由一种过渡金属和各种配体组成的人造系统形成的，我们在其上进行了DMRG并计算了单位熵。在选定的系统集合中，从小基准分子到涉及两个金属中心的较大挑战系统，我们证明我们的ML模型可以正确预测具有高精度的轨道的重要性。此外，ML模型对系统的可传递性高于训练程序中使用的任何复合物。
The active-space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial, but a non-trivial task. In this article, we present the neural network (NN) based approach for automatic selection of active spaces, focused on transition metal systems. The training set has been formed from artificial systems composed from one transition metal and various ligands, on which we have performed DMRG and calculated single-site entropy. On the selected set of systems, ranging from small benchmark molecules up to larger challenging systems involving two metallic centers, we demonstrate that our ML models could correctly predict the importance of orbitals with the high accuracy. Also, the ML models show a high degree of transferability on systems much larger than any complex used in training procedures.
