Machine learning hydrogen adsorption on nanoclusters through structural descriptors

Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures.Machine learning reduces the cost for modelling those sites with the aid of descriptors.We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions,Many-Body Tensor Representation and Atom-Centered Symmetry Functions while predicting the hydrogen adsorption(free)energy on the surface of nanoclusters.The 2D-material molybdenum disulphide and the alloy copper–gold functioned as test systems.Potential energy scans of hydrogen on the cluster surfaces were conducted to compare the accuracy of the descriptors in kernel ridge regression.By having recourse to data sets of 91 molybdenum disulphide clusters and 24 copper–gold clusters,we found that the mean absolute error could be reduced by machine learning on different clusters simultaneously rather than separately.The adsorption energy was explained by the local descriptor Smooth Overlap of Atomic Positions,combining it with the global descriptor Many-Body Tensor Representation did not improve the overall accuracy.We concluded that fitting of potential energy surfaces could be reduced significantly by merging data from different nanoclusters.

Materials structure genealogy and high-throughput topological classification of surfaces and 2D materials

Automated and verifiable structural classification for atomistic structures is becoming necessary to cope with the vast amount of information stored in various computational materials databases.Here we present a general recursive scheme for the structural classification of atomistic systems and introduce a structural materials map that can be used to organize the materials structure genealogy.We also introduce our implementation for the automatic classification of two-dimensional structures,especially focusing on surfaces and 2D materials.This classification procedure can automatically determine the dimensionality of a structure,further categorize the structure as a surface or a 2D material,return the underlying unit cell and also identify the outlier atoms,such as adsorbates.The classification scheme does not require explicit search patterns and works even in the presence of defects and dislocations.The classification is tested on a wide variety of atomistic structures and provides a high-accuracy determination for all of the returned structural properties.A software implementation of the classification algorithm is freely available with an opensource license.

One-, Two-, and Three-Dimensional Supramolecular Assemblies Based on Tubular and Regular Polygonal Structures of Pillar[n]arenes

Pillar[n]arenes,which were first reported by our groupin2008,arepromisingmacrocycliccompounds in supramolecular chemistry.The simple,tubular,and highly symmetrical shape of pillar[n]arenes has allowed various supramolecular assemblies with well-defined structures to be constructed.