arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2011.14715physics.chem-ph
收藏

基于机器学习算法的强相关系统活动空间的自动选择

Automatic selection of active spaces for strongly correlated systems using machine learning algorithms

Pavlo Golub, Andrej Antalik, Libor Veis, Jiri Brabec

摘要:

活性空间量子化学方法可以对强关联电子系统提供非常精确的描述,具有重要的自然科学价值。正确选择活动空间是至关重要的,但也是一项重要的任务。在这篇文章中,我们提出了基于神经网络(NN)的方法自动选择的活性空间,重点是过渡金属系统。训练集是由一种过渡金属和多种配体组成的人工体系形成的,我们在其上进行了DMRG并计算了单位点熵。在选定的一组系统上,从小的基准分子到涉及两个金属中心的较大的具有挑战性的系统,我们证明了我们的ML模型可以高精度地正确预测轨道的重要性。此外,ML模型在比训练过程中使用的任何复杂系统都大得多的系统上显示出高度的可移植性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑