论文标题
通过基于深度能量的模型产生簇
Cluster Generation via Deep Energy-Based Model
论文作者
论文摘要
我们提出了一种新的方法，用于使用深度学习方法生成纳米群体的稳定结构。我们的方法包括构建人造势能表面，局部最小值对应于最稳定的结构，并且在配置空间的中间区域中比“真实”潜力更光滑。为了构建表面，使用图形卷积网络。该方法可以将潜在的表面推断到与训练中使用的原子数量更大的结构的情况。因此，在训练集中拥有足够数量的低能结构，该方法允许为地面结构（包括较大原子的结构）生成新的候选物。我们将方法应用于二氧化硅簇$（SIO_2）_n $，并首次找到了n = 28 ... 51的稳定结构。该方法是通用的，不取决于原子的原子数量。
We present a new approach for the generation of stable structures of nanoclusters using deep learning methods. Our method consists in constructing an artificial potential energy surface, with local minima corresponding to the most stable structures and which is much smoother than "real" potential in the intermediate regions of the configuration space. To build the surface, graph convolutional networks are used. The method can extrapolates the potential surface to cases of structures with larger number of atoms than was used in training. Thus, having a sufficient number of low-energy structures in the training set, the method allows to generate new candidates for the ground-state structures, including ones with larger number of atoms. We applied the approach to silica clusters $(SiO_2)_n$ and for the first time found the stable structures with n=28...51. The method is universal and does not depend on the atomic composition and number of atoms.
