高维神经网络势与系统分子裂解法的准确性比较：全反式烷烃的基准研究
Comparing the Accuracy of High-Dimensional Neural Network Potentials and the Systematic Molecular Fragmentation Method: A Benchmark Study for all-trans Alkanes
摘要：
许多方法，已被开发来表达大系统的势能，利用原子相互作用的局部性。一个突出的例子是碎片化方法，其中对给定分子的重叠小片段进行量子化学计算，然后在第二步中组合以产生系统的总能量。在这里，我们比较了系统的分子片段化方法的准确性与性能的高维神经网络（HDNN）的潜力介绍Behler和Parrinello。HDNN势在精神上类似于碎裂方法，因为总能量被构造为环境依赖的原子能的总和，其间接来自电子结构计算。作为一个基准集，我们使用的全反式烷烃含有多达11个碳原子的耦合集群理论水平。选择这些分子是因为它们可以外推非常长链的可靠参考能量，从而能够评估通过两种方法获得的包括多达10 000个碳原子的烷烃的能量。我们发现，这两种方法预测高质量的能量与HDNN潜力产生较小的误差相对于耦合集群参考。
英文摘要：
Many approaches, which have been developed to express the potential energy of large systems, exploit the locality of the atomic interactions. A prominent example are fragmentation methods, in which quantum chemical calculations are carried out for overlapping small fragments of a given molecule that are then combined in a second step to yield the system's total energy. Here we compare the accuracy of the systematic molecular fragmentation approach with the performance of high-dimensional neural network (HDNN) potentials introduced by Behler and Parrinello. HDNN potentials are similar in spirit to the fragmentation approach in that the total energy is constructed as a sum of environment-dependent atomic energies, which are derived indirectly from electronic structure calculations. As a benchmark set we use all-trans alkanes containing up to eleven carbon atoms at the coupled cluster level of theory. These molecules have been chosen because they allow to extrapolate reliable reference energies for very long chains, enabling an assessment of the energies obtained by both methods for alkanes including up to 10 000 carbon atoms. We find that both methods predict high-quality energies with the HDNN potentials yielding smaller errors with respect to the coupled cluster reference.
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