Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany.
J Chem Phys. 2012 Feb 14;136(6):064103. doi: 10.1063/1.3682557.
Understanding the unique properties of water still represents a significant challenge for theory and experiment. Computer simulations by molecular dynamics require a reliable description of the atomic interactions, and in recent decades countless water potentials have been reported in the literature. Still, most of these potentials contain significant approximations, for instance a frozen internal structure of the individual water monomers. Artificial neural networks (NNs) offer a promising way for the construction of very accurate potential-energy surfaces taking all degrees of freedom explicitly into account. These potentials are based on electronic structure calculations for representative configurations, which are then interpolated to a continuous energy surface that can be evaluated many orders of magnitude faster. We present a full-dimensional NN potential for the water dimer as a first step towards the construction of a NN potential for liquid water. This many-body potential is based on environment-dependent atomic energy contributions, and long-range electrostatic interactions are incorporated employing environment-dependent atomic charges. We show that the potential and derived properties like vibrational frequencies are in excellent agreement with the underlying reference density-functional theory calculations.
理解水的独特性质仍然是理论和实验的重大挑战。分子动力学的计算机模拟需要对原子相互作用进行可靠的描述，在最近几十年中，文献中报道了无数种水势能。尽管如此，这些势能大多包含重大的近似值，例如单个水分子的冻结内部结构。人工神经网络 (NN) 为构建非常精确的势能面提供了有希望的方法，该势能面明确考虑了所有自由度。这些势能基于代表性构型的电子结构计算，然后对其进行内插以获得可以快得多的评估许多数量级的连续能面。我们提出了一个全维 NN 水二聚体势，作为构建液体水的 NN 势的第一步。这个多体势基于依赖环境的原子能量贡献，并且通过使用依赖环境的原子电荷来合并长程静电相互作用。我们表明，该势能和得出的性质，如振动频率，与基础参考密度泛函理论计算非常吻合。
