Wang Zedong, Dong Jiawei, Qiu Jing, Wang Linjun
Key Laboratory of Excited-State Materials of Zhejiang Province, Department of Chemistry, Zhejiang University, Hangzhou 310027, China.
ACS Appl Mater Interfaces. 2022 May 25;14(20):22929-22940. doi: 10.1021/acsami.1c22181. Epub 2022 Jan 31.
Trajectory surface hopping combined with ab initio electronic structure calculations is a popular and powerful approach for on-the-fly nonadiabatic dynamics simulations. For large systems, however, this remains a significant challenge because of the unaffordable computational cost of large-scale electronic structure calculations. Here, we present an efficient divide-and-conquer approach to construct the system Hamiltonian based on Wannier analysis and machine learning. In detail, the large system under investigation is first decomposed into small building blocks, and then all possible segments formed by building blocks within a cutoff distance are found out. Ab initio molecular dynamics is carried out to generate a sequence of geometries for each equivalent segment with periodicity. The Hamiltonian matrices in the maximum localized Wannier function (MLWF) basis are obtained for all geometries and utilized to train artificial neural networks (ANNs) for the structure-dependent Hamiltonian elements. Taking advantage of the orthogonality and spatial locality of MLWFs, the one-electron Hamiltonian of a large system at arbitrary geometry can be directly constructed by the trained ANNs. As demonstrations, we study charge transport in a zigzag graphene nanoribbon (GNR), a coved GNR, and a series of hybrid GNRs with a state-of-the-art surface hopping method. The interplay between delocalized and localized states is found to determine the electron dynamics in hybrid GNRs. Our approach has successfully studied GNRs with >10 000 atoms, paving the way for efficient and reliable all-atom nonadiabatic dynamics simulation of general systems.
轨迹表面跳跃结合从头算电子结构计算是一种流行且强大的实时非绝热动力学模拟方法。然而，对于大型系统而言，这仍然是一项重大挑战，因为大规模电子结构计算的计算成本过高。在此，我们提出一种基于万尼尔分析和机器学习构建系统哈密顿量的高效分治方法。具体而言，首先将所研究的大型系统分解为小的构建块，然后找出截止距离内由构建块形成的所有可能片段。进行从头算分子动力学以生成每个等效片段具有周期性的一系列几何结构。针对所有几何结构获得最大局域化万尼尔函数（MLWF）基下的哈密顿矩阵，并用于训练人工神经网络（ANN）以得到与结构相关的哈密顿量元素。利用MLWF的正交性和空间局部性，通过训练好的ANN可以直接构建任意几何结构下大型系统的单电子哈密顿量。作为示例，我们使用一种先进的表面跳跃方法研究锯齿形石墨烯纳米带（GNR）、弯曲GNR以及一系列混合GNR中的电荷传输。发现离域态和局域态之间的相互作用决定了混合GNR中的电子动力学。我们的方法已成功研究了原子数超过10000的GNR，为通用系统的高效可靠全原子非绝热动力学模拟铺平了道路。
