First principles global optimization of metal clusters and nanoalloys

Abstract

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 possibility of using more rigorous quantum chemical first principle methods during the global optimization has been facilitated by the development of more powerful computer hardware as well as more efficient algorithms. In this review, recent advances in first principle global optimization methods are described, with the main focus on genetic algorithms coupled with density functional theory for optimizing sub-nanometre metal clusters and nanoalloys.

Size, Composition, and Support-Doping Effects on Oxygen Reduction Activity of Platinum-Alloy and on non-Platinum Metal-Decorated-Graphene Nanocatalysts

“…In general, small, positive formation energies are necessary to form these bigger TMNP-MDG systems, as shown in the literature. [131][132][133][134][135][136][137][138][139][140][141][142] While increasing the size from 4 to 7 atoms implies slightly higher energy; the results show the opposite for the 19-atom NP. In all the alloy NP studied, the formation energies decrease significantly when forming 19-atom particles, showing up to 0.5 eV less required to form 19-atom Rh-Ir NP for example, as compared to the corresponding smaller Rh-Ir TMNP-MDG.…”

NWPEsSe: an Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems

“…Examples of such methods rely on simulated annealing, 9 Monte Carlo minimization, 10 basin hopping, [11][12][13][14][15] genetic algorithms, [16][17][18][19][20][21][22][23] particle swarm optimization, 24 stochastic surface walking, 25 kick method, [26][27][28][29] GIGA, 30 and have shown good performance for various chemical problems. We also refer the readers to reviews 31,32 or our previous papers 33, 34 for a comprehensive discussion. Since 2004, instead of empirical force-fields, reliable first-principles methods began to appear for calculating cluster energies.…”

GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands

“…In modern nanoscience, great efforts are being made to globally optimise nanosystems, such as clusters, to find the geometry with the overall lowest energy, the so-called global minimum (GM). [1][2][3][4][5] On the one hand, global optimisation (GO) of nanoparticles (NPs) plays an important role in rational materials design, in order to tailor material properties for future applications. On the other hand, the GM is, from a thermodynamic point of view, the most stable structure (at T = 0 K) and often the most likely candidate to be formed in an experiment.…”

“…5,[7][8][9][10][11] Genetic algorithms have been successfully applied to the global optimisation of many clusters and NPs. 1,2,5,12 The origin of the GA presented here is the Birmingham Cluster Genetic Algorithm (BCGA), a Lamarckian-type GA developed for the GO of free pure and mixed clusters using plane-wave density functional theory (pw-DFT) or empirical potentials (EPs). 5,13 Subsequently, a surface mode (S-BCGA) was introduced.…”