The European Physical Journal B ( IF 1.6 ) Pub Date : 2021-07-19 , DOI: 10.1140/epjb/s10051-021-00156-1 Jörg Behler 1 , Gábor Csányi 2
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Abstract
In the past two and a half decades machine learning potentials have evolved from a special purpose solution to a broadly applicable tool for large-scale atomistic simulations. By combining the efficiency of empirical potentials and force fields with an accuracy close to first-principles calculations they now enable computer simulations of a wide range of molecules and materials. In this perspective, we summarize the present status of these new types of models for extended systems, which are increasingly used for materials modelling. There are several approaches, but they all have in common that they exploit the locality of atomic properties in some form. Long-range interactions, most prominently electrostatic interactions, can also be included even for systems in which non-local charge transfer leads to an electronic structure that depends globally on all atomic positions. Remaining challenges and limitations of current approaches are discussed.
Graphic Abstract
中文翻译：
扩展系统的机器学习潜力：一个观点
摘要
在过去的两年半中，机器学习潜力已经从一种特殊用途的解决方案发展为一种广泛适用的大规模原子模拟工具。通过将经验势和力场的效率与接近第一性原理计算的精度相结合，它们现在可以对各种分子和材料进行计算机模拟。从这个角度来看，我们总结了这些新型扩展系统模型的现状，这些模型越来越多地用于材料建模。有几种方法，但它们都有一个共同点，即它们以某种形式利用原子属性的局部性。长程相互作用，最突出的静电相互作用，即使对于非局部电荷转移导致电子结构全局依赖于所有原子位置的系统，也可以包括在内。讨论了当前方法的剩余挑战和局限性。
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