Growth mechanism prediction for nanoparticles via structure matching polymerization

Abstract

Exploring structural and component evolution remains a challenging scientific problem for nanoscience. We propose a novel approach called principle of minimization of structure matching polymerization (SMP) change to rapidly explore the global minimum structure on the potential energy surface (PES). The new method can map low-dimensional stable structures to high-dimensional local minima, and this will make it possible for us to study the growth mechanisms of nanoparticles. Some new lowest-energy structures were found by SMP methods for sulfuric acid (SA)–dimethylamine (DMA) systems relative to previous studies. Additionally, we found that the growth process of boron clusters is mainly that the small-size boron clusters are continuously added to large-size boron clusters by structure matching for B_n (n = 2–36) systems, B_m + B_k → B_n, where m + k = n and 1 ≤ k ≤ 3. The SMP approach can greatly improve the search efficiency of other unbiased global optimization algorithms, such as basin-hopping (BH) and genetic algorithm (GA), with an enhancement of up to 19- and 7-fold relative to traditional BH and GA algorithms for searching the global minima of B_n (n = 14–22) systems. The SMP approach is general and flexible and can be applied to different kinds of problems, such as material structure design, crystal structure prediction, and new drug generation.