Perspective: Machine learning potentials for atomistic simulations
Perspective: Machine learning potentials for atomistic simulations
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DOI：
10.1063/1.4966192
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发表时间：
2016-11-07
影响因子：
4.4
通讯作者：
Behler, Joerg
中科院分区：
文献类型：
--
作者：
Behler, Joerg
如今，计算机模拟已经成为化学、凝聚态物理和材料科学几乎所有领域的标准工具。为了跟上最先进的实验和所研究问题的不断增长的复杂性，对更真实的、即更大的、具有更高精度的模型系统的模拟的需求不断增加。在许多情况下，提供可靠能量和力的足够有效的原子间势的可用性已经成为进行这些模拟的严重瓶颈。为了解决这个问题，目前原子间势的发展正在发生一种范式的变化。自从早期的计算机模拟以来，只要直接应用电子结构方法的要求太高，就会使用物理近似来推导简化势。机器学习(ML)的最新进展现在提供了一种通过拟合来自电子结构计算的大数据集来表示势能面的替代方法。从这一角度出发，我们回顾了这些ML潜力背后的核心思想、已解决的问题和仍然存在的挑战，并讨论了它们目前的适用性和局限性。由AIP出版公司出版。
Nowadays, computer simulations have become a standard tool in essentially all fields of chemistry, condensed matter physics, and materials science. In order to keep up with state-of-the-art experiments and the ever growing complexity of the investigated problems, there is a constantly increasing need for simulations of more realistic, i.e., larger, model systems with improved accuracy. In many cases, the availability of sufficiently efficient interatomic potentials providing reliable energies and forces has become a serious bottleneck for performing these simulations. To address this problem, currently a paradigm change is taking place in the development of interatomic potentials. Since the early days of computer simulations simplified potentials have been derived using physical approximations whenever the direct application of electronic structure methods has been too demanding. Recent advances in machine learning (ML) now offer an alternative approach for the representation of potential-energy surfaces by fitting large data sets from electronic structure calculations. In this perspective, the central ideas underlying these ML potentials, solved problems and remaining challenges are reviewed along with a discussion of their current applicability and limitations. Published by AIP Publishing.