论文标题
原子配置交互问题的深度学习方法很大
Deep-learning approach for the atomic configuration interaction problem on large basis sets
论文作者
论文摘要
高精度原子结构的计算需要通常通过配置相互作用（CI）问题在多构型波函数扩展上解决电子相关性的准确建模。即使对于高级超级计算机，后者也很容易变得具有挑战性或不可或缺。在这里，我们开发了一种深度学习方法，该方法允许以较大的CI基集的最相关配置进行预先选择，直到达到目标能量精度为止。因此，大型CI计算被一系列由神经网络管理的迭代扩展基集执行的较小较小的计算。虽然量子化学中使用的密集体系结构失败，但我们表明卷积神经网络自然说明了基集的物理结构，并允许进行稳健而准确的CI计算。该方法是根据中等大小的基础基准测试的，允许进行直接的CI计算，并在无法直接计算的情况下进一步证明。
High-precision atomic structure calculations require accurate modelling of electronic correlations typically addressed via the configuration interaction (CI) problem on a multiconfiguration wave function expansion. The latter can easily become challenging or infeasibly large even for advanced supercomputers. Here we develop a deep-learning approach which allows to preselect the most relevant configurations out of large CI basis sets until the targeted energy precision is achieved. The large CI computation is thereby replaced by a series of smaller ones performed on an iteratively expanding basis subset managed by a neural network. While dense architectures as used in quantum chemistry fail, we show that a convolutional neural network naturally accounts for the physical structure of the basis set and allows for robust and accurate CI calculations. The method was benchmarked on basis sets of moderate size allowing for the direct CI calculation, and further demonstrated on prohibitively large sets where the direct computation is not possible.
