Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria.
J Chem Phys. 2013 Oct 28;139(16):164105. doi: 10.1063/1.4825111.
The accurate identification and classification of local ordered and disordered structures is an important task in atomistic computer simulations. Here, we demonstrate that properly trained artificial neural networks can be used for this purpose. Based on a neural network approach recently developed for the calculation of energies and forces, the proposed method recognizes local atomic arrangements from a set of symmetry functions that characterize the environment around a given atom. The algorithm is simple and flexible and it does not rely on the definition of a reference frame. Using the Lennard-Jones system as well as liquid water and ice as illustrative examples, we show that the neural networks developed here detect amorphous and crystalline structures with high accuracy even in the case of complex atomic arrangements, for which conventional structure detection approaches are unreliable.
准确识别和分类局部有序和无序结构是原子计算机模拟中的一项重要任务。在这里，我们证明了经过适当训练的人工神经网络可用于此目的。基于最近开发的一种用于计算能量和力的神经网络方法，该方法从一组对称函数中识别局部原子排列，这些对称函数描述了给定原子周围的环境。该算法简单灵活，不依赖于参考系的定义。使用 Lennard-Jones 系统以及液态水和冰作为说明性示例，我们表明，即使对于原子排列复杂的情况，这里开发的神经网络也可以高精度地检测非晶态和晶态结构，而对于传统的结构检测方法，这种情况是不可靠的。
