Evolutionary algorithms such as the basin-hopping (BH) algorithm have proven to be useful
for difficult non-linear optimization problems with multiple modalities and variables …

The global optimization of metal cluster structures is an important research field. The
traditional deep neural network (T-DNN) global optimization method is a good way to find …

From as small as single carbon dimers up to giant fullerenes or amorphous nanometer-sized
particles, the large family of carbon nanoclusters holds a complex structural variability that …

We have developed an algorithm to automatically build the global minimum and other low-
energy minima of nanoclusters. This method is implemented in PyAR (https://github …

Recently, novel algorithms and enhanced computational capabilities have created
unprecedented opportunities to precisely determine the geometric structures of clusters …

The identification of species in the interstellar medium requires precise and molecule-
specific spectroscopic information in the laboratory framework, in broad spectral ranges and …

A detailed exploration of the energy landscape is a necessary criterion for an efficient
structure prediction. Although computational power has increased many-folds in recent …

Searching the global minimum (GM) structures of metal clusters is of great importance in
cluster science. Very recently, the global optimization method based on deep neural network …

The identification of species in the interstellar medium requires precise and molecule-
specific spectroscopic information in the laboratory framework, in broad spectral ranges and …

Structural prediction of low-energy isomers of carbon twelve-atom clusters is carried out
using the recently developed machine-learning potential GAP-20. The GAP-20 agrees with …