论文标题
定向自适应最小学习机：应用于硫醇盐保护的金纳米簇和金硫醇酸盐环
Orientation Adaptive Minimal Learning Machine: Application to Thiolate-Protected Gold Nanoclusters and Gold-Thiolate Rings
论文作者
论文摘要
机器学习（ML）力场是ML方法在物理和化学科学领域中最常见的应用之一。在最佳情况下，他们能够达到与计算成本大大降低的第一原理方法的准确性。但是，ML方法的训练通常依赖于其势能以及其势能以及将力信息应用于标准算法需要特殊修改的完整原子结构。在这里，我们采用基于距离的ML方法来预测力规范并估计受硫醇盐保护的金纳米簇的力量的方向。该方法仅依赖于无需能源评估的局部结构信息。我们将原子ML力应用于金硫酸盐环的结构优化，$ \ text {au} _ {25}（\ text {sch} _ {3}）_ {18} $ nanocluster和两个已知的结构性isomers $ \ text {au} _ {38}（\ text {sch} _ {3}）_ {24} $ nanocluster。结果表明，该方法非常适合于金硫醇系统的结构优化，在该系统中，原子键在配体壳和金属配体界面中具有共价性质。该方法可以看作是为基于距离的ML方法引入地位学习的早期尝试。
Machine learning (ML) force fields are one of the most common applications of ML methods in the field of physical and chemical science. In the optimal case, they are able to reach accuracy close to the first principles methods with significantly lowered computational cost. However, often the training of the ML methods rely on full atomic structures alongside their potential energies, and applying the force information needs special modifications to standard algorithms. Here we apply distance-based ML methods to predict force norms and estimate the directions of the force vectors of the thiolate-protected gold nanoclusters. The method relies only on local structural information without energy evaluations. We apply the atomic ML forces on the structure optimization of the gold-thiolate rings, $\text{Au}_{25}(\text{SCH}_{3})_{18}$ nanocluster and two known structural isomers of the $\text{Au}_{38}(\text{SCH}_{3})_{24}$ nanocluster. The results demonstrate that the method is well-suited for the structural optimizations of the gold-thiolate systems, where the atomic bonding has a covalent nature in the ligand shell and at the metal-ligand interface. The methodology could be seen as an early attempt to introduce equivariant learning to distance-based ML methods.
