The global optimization of nanoparticles, such as pure or bimetallic metal clusters, has
become a very important and sophisticated research field in modern nanoscience. The …

Nanoalloys are bi-or multi-metallic nanoparticles with sizes in the range between 1 and 100
nm. They are the subject of intense research activity in the last decades, both in experiments …

Machine learning (ML) is used to derive local stability information for density functional
theory calculations of systems in relation to the recently discovered SnO 2 (110)-(4× 1) …

We present the implementation of GAtor, a massively parallel, first-principles genetic
algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and …

The ability to navigate vast energy landscapes of molecules, clusters, and solids is a
necessity for discovering novel compounds in computational chemistry and materials …

Genetic algorithms (GAs) are stochastic global search methods inspired by biological
evolution. They have been used extensively in chemistry and materials science coupled with …

Predicting structures at the atomic scale is of great importance for understanding the
properties of materials. Such predictions are infeasible without efficient global optimization …

In this article, we introduce an efficient global‐minimum structural search program named
Tsinghua Global Minimum 2 (TGMin‐2), which is the successor of the original TGMin …

New algorithms for the optimization of alloy nanoparticles (nanoalloys) are presented. The
new algorithms are based on the concept of multiple basin‐hopping walkers running in …

We have developed an evolutionary algorithm (EA) for the global minimum search of
molecular clusters. The EA is able to discover all the putative global minima of water clusters …