Development of a generally applicable machine learning potential with accurate long-range electrostatic interactions

近年来，基于机器学习技术的新一代原子间势被引入。这些势提供了原子位置和势能之间的直接函数关系，结合了电子结构方法的准确性和简单经验势的效率。由于缺乏特定于系统的术语，它们允许对大量不同的系统进行扩展模拟。这些势中的大多数依赖于原子的性质，如能量和电荷，仅取决于原子的局部化学环境。然而，这种局部电荷不能捕获远程电荷转移。这阻碍了对系统的准确描述，在该系统中，遥远的结构特征对系统中的电荷分布具有全局影响。这类系统的例子是包括缺陷的半导体、氧化物的极性表面和具有不同可能的金属氧化状态的金属-有机分子。为了克服当前机器学习潜力的这些内在局限性，我们提出将高维神经网络与电荷平衡神经网络技术相结合。由此产生的新方法将普遍适用于所有类型的系统，我们将通过使用最小跳跃方法分析涵盖所有类型键的不同模型系统的势能面来演示这一点。

In recent years, a new generation of interatomic potentials based on machine learning techniques has been introduced. These potentials, which provide a direct functional relation between the atomic positions and the potential-energy, combine the accuracy of electronic structure methods with the efficiency of simple empirical potentials. Because of the absence of system-specific terms they allow to perform extended simulations of a large variety of systems. Most of these potentials rely on atomic properties like energies and charges depending only on the local chemical environments of the atoms. Such local charges are, however, unable to capture long-range charge transfer. This prevents the accurate description of systems in which distant structural features have global effects on the charge distribution in the system. Examples for such systems are semiconductors including defects, polar surfaces of oxides and metal-organic molecules with different possible metal oxidation states. In order to overcome these intrinsic limitations of current machine learning potentials, we propose to combine high-dimensional neural networks with the charge equilibration neural network technique. The resulting new method will be generally applicable to all types of systems, which we will demonstrate by analyzing the potential-energy surfaces of different model systems covering all types of bonding using the minima hopping method.