论文标题
基于神经网络驱动技术的二维材料的结构预测
Structure prediction of two-dimensional materials based on neural network-driven evolutionary technique
论文作者
论文摘要
我们提出了一种简单而有效的方法，用于二维结构的结构预测。该方法基于神经网络和进化技术的组合。它允许找到原始的2D结构以及在基板上生长的结构。进行的测试表明，该方法是有效的，并且仅基于化学计量的信息的计算可以导致稳定的结构。由于该算法能够解决给定底物上的结构，因此从实验的角度来看可能很有用。
We present a simple yet effective method for structure prediction of two-dimensional structures. The method is based on a combination of neural networks and evolutionary techniques. It allows finding pristine 2D structures as well as structures grown on a substrate. Conducted tests show, that the method is efficient and the calculations, based only on the information of stoichiometry, can lead to stable structures. Since the algorithm is able to address structures on a given substrate, it can be useful from the experimental point of view.
