Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany.
Phys Chem Chem Phys. 2011 Oct 28;13(40):17930-55. doi: 10.1039/c1cp21668f. Epub 2011 Sep 13.
The accuracy of the results obtained in molecular dynamics or Monte Carlo simulations crucially depends on a reliable description of the atomic interactions. A large variety of efficient potentials has been proposed in the literature, but often the optimum functional form is difficult to find and strongly depends on the particular system. In recent years, artificial neural networks (NN) have become a promising new method to construct potentials for a wide range of systems. They offer a number of advantages: they are very general and applicable to systems as different as small molecules, semiconductors and metals; they are numerically very accurate and fast to evaluate; and they can be constructed using any electronic structure method. Significant progress has been made in recent years and a number of successful applications demonstrate the capabilities of neural network potentials. In this Perspective, the current status of NN potentials is reviewed, and their advantages and limitations are discussed.
分子动力学或蒙特卡罗模拟得到的结果的准确性，关键取决于对原子相互作用的可靠描述。文献中提出了大量有效的势函数，但通常最优的函数形式很难找到，而且强烈依赖于特定的体系。近年来，人工神经网络（NN）已成为构建广泛体系势能的一种很有前途的新方法。它们具有许多优点：它们非常通用，适用于小分子、半导体和金属等不同的体系；数值上非常准确，计算速度也很快；并且可以使用任何电子结构方法来构建。近年来已经取得了重大进展，许多成功的应用证明了神经网络势的能力。在这篇观点文章中，回顾了 NN 势的现状，并讨论了它们的优点和局限性。
