Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
复制标题
DOI：
10.1039/c1cp21668f
复制
发表时间：
2011-01-01
影响因子：
3.3
通讯作者：
Behler, Joerg
中科院分区：
文献类型：
--
作者：
Behler, Joerg
在分子动力学或蒙特卡罗模拟中获得的结果的准确性关键取决于对原子相互作用的可靠描述。文献中已经提出了各种各样的有效势，但通常难以找到最优的函数形式，并且强烈依赖于特定的系统。近年来，人工神经网络（NN）已成为一种很有前途的构建各种系统电位的新方法。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系统；它们在数值上非常准确，计算速度也很快；它们可以用任何电子结构方法来构造。近年来取得了重大进展，许多成功的应用证明了神经网络潜力的能力。本文综述了神经网络电位的研究现状，讨论了它们的优点和局限性。
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.