Dietz C, Kretz T, Thoma M H
I. Physikalisches Institut, Justus Liebig Universität Giessen, Heinrich-Buff-Ring 16, D 35392 Giessen, Germany.
Phys Rev E. 2017 Jul;96(1-1):011301. doi: 10.1103/PhysRevE.96.011301. Epub 2017 Jul 19.
Machine learning is one of the most popular fields in computer science and has a vast number of applications. In this work we will propose a method that will use a neural network to locally identify crystal structures in a mixed phase Yukawa system consisting of fcc, hcp, and bcc clusters and disordered particles similar to plasma crystals. We compare our approach to already used methods and show that the quality of identification increases significantly. The technique works very well for highly disturbed lattices and shows a flexible and robust way to classify crystalline structures that can be used by only providing particle positions. This leads to insights into highly disturbed crystalline structures.
机器学习是计算机科学中最热门的领域之一，有着大量的应用。在这项工作中，我们将提出一种方法，该方法将使用神经网络在由面心立方（fcc）、六方密堆积（hcp）和体心立方（bcc）簇以及类似于等离子体晶体的无序粒子组成的混合相 Yukawa 系统中局部识别晶体结构。我们将我们的方法与已使用的方法进行比较，并表明识别质量显著提高。该技术对于高度紊乱的晶格效果很好，并展示了一种灵活且稳健的晶体结构分类方法，仅通过提供粒子位置即可使用。这为深入了解高度紊乱的晶体结构提供了思路。
