通过多体展开表示水相互作用中置换不变多项式、神经网络和高斯近似势的比较
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
摘要：
多维势能面的精确表示是分子系统真实计算机模拟的必要条件。随着计算机能力的不断提高，以及相关电子结构方法的进步，现在可以对小系统进行精确的相互作用能的常规计算，然后可以将其用作开发严格来自多体展开的分析势能函数（PEF）的参考。基于MB-pol多体PEF的准确性，我们研究了置换不变多项式、神经网络和高斯近似势在表示水的两体和三体相互作用能方面的性能，分别表示所产生的势PIP-MB-pol、BPNN-MB-pol和GAP-MB-pol。我们的分析表明，所有三种分析表示在再现两体和三体参考数据以及从理论的耦合簇水平进行的计算中获得的小水簇的相互作用能方面表现出相似的准确性水平，这是目前化学准确性的黄金标准。这些结果表明，在多体膨胀，如MB-pol，这是物理上健全的和可转移的，和机器学习技术，提供了一个灵活的框架，以近似的短程相互作用能量项制定的原子间的潜力之间的协同作用。
英文摘要：
The accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase in computer power accompanied by advances in correlated electronic structure methods nowadays enable routine calculations of accurate interaction energies for small systems, which can then be used as references for the development of analytical potential energy functions (PEFs) rigorously derived from many-body expansions. Building on the accuracy of the MB-pol many-body PEF, we investigate here the performance of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water two-body and three-body interaction energies, denoting the resulting potentials PIP-MB-pol, BPNN-MB-pol, and GAP-MB-pol, respectively. Our analysis shows that all three analytical representations exhibit similar levels of accuracy in reproducing both two-body and three-body reference data as well as interaction energies of small water clusters obtained from calculations carried out at the coupled cluster level of theory, the current gold standard for chemical accuracy. These results demonstrate the synergy between interatomic potentials formulated in terms of a many-body expansion, such as MB-pol, that are physically sound and transferable, and machine-learning techniques that provide a flexible framework to approximate the short-range interaction energy terms.
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