In the past two decades, machine learning potentials (MLPs) have reached a level of maturity that now enables applications to large-scale atomistic simulations of a wide range of systems in chemistry, physics, and materials science. Different machine learning algorithms have been used with great success in the construction of these MLPs. In this review, we discuss an important group of MLPs relying on artificial neural networks to establish a mapping from the atomic structure to the potential energy. In spite of this common feature, there are important conceptual differences among MLPs, which concern the dimensionality of the systems, the inclusion of long-range electrostatic interactions, global phenomena like nonlocal charge transfer, and the type of descriptor used to represent the atomic structure, which can be either predefined or learnable. A concise overview is given along with a discussion of the open challenges in the field.

中文翻译：

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神经网络势：方法简述

在过去的二十年中，机器学习潜力 (MLP) 已经达到了成熟的水平，现在可以应用于化学、物理和材料科学领域各种系统的大规模原子模拟。不同的机器学习算法在这些 MLP 的构建中得到了巨大的成功。在这篇综述中，我们讨论了一组重要的 MLP，它们依靠人工神经网络来建立从原子结构到势能的映射。尽管有这个共同特征，MLP 之间还是存在重要的概念差异，这些差异涉及系统的维数、长程静电相互作用的包含、非局域电荷转移等全局现象以及用于表示原子结构的描述符类型，它可以是预定义的或可学习的。给出了简明的概述，并讨论了该领域的开放挑战。