Orientation Adaptive Minimal Learning Machine: Application to Thiolate-Protected Gold Nanoclusters and Gold-Thiolate Rings

Abstract

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 is difficult especially in the case of kernel-based methods. Here we apply distance-based ML methods to predict force norms and estimate the directions of the force vectors of the thiolate-protected Au 38 (SCH 3 ) 24 nanocluster. 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 and partial optimization of two known structural isomers of the Au 38 (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.

Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts

“…26 Distance-based ML methods have been proven to be able to create realistic potentials. 27,28 Support vector machines have been utilized to evaluate the fluorescence properties of MPCs, 29,30 and recently convolutional neural networks have been able to detect features in UV−vis spectra of mixtures of different sized thiolate protected clusters. 31 In this study, we utilized four different kernel-based ML methods, minimal learning machine (MLM), 32,33 minimal learning machine (EMLM), 34,35 kernelized ridge regression (KRR), 36,37 and learning kernel ridge regression (LKRR), 38 to predict hydrogen interaction energies on [M x Au 25−x (SCH 3 ) 18 + H] q (M ∈ {Pd, Cu}, x ∈ {0, 1} and q ∈ [−2, 2]) systems.…”