论文标题
使用具有分数存在的原子对原子结构的机器学习实现了优化
Machine-learning enabled optimization of atomic structures using atoms with fractional existence
论文作者
论文摘要
我们介绍了一种全局优化原子系统结构的方法，该结构使用具有分数存在的其他原子。该方法允许在常规位置空间中遇到的能量屏障的长距离移动原子。该方法基于高斯过程，在该过程中，用矢量指纹进行了对分数存在的外推。该方法应用于簇和二维系统，在该系统中，在将原子位置固定在晶格上的同时，优化了分数存在变量。在不同大小的铜簇上证明了原子坐标和存在变量的同时优化。证明存在变量可以加快大型，特别难以优化的簇的全局优化。
We introduce a method for global optimization of the structure of atomic systems that uses additional atoms with fractional existence. The method allows for movement of atoms over long distances bypassing energy barriers encountered in the conventional position space. The method is based on Gaussian processes, where the extrapolation to fractional existence is performed with a vectorial fingerprint. The method is applied to clusters and two-dimensional systems, where the fractional existence variables are optimized while keeping the atomic positions fixed on a lattice. Simultaneous optimization of atomic coordinates and existence variables is demonstrated on copper clusters of varying size. The existence variables are shown to speed up the global optimization of large and particularly difficult-to-optimize clusters.
