Growth mechanism prediction for nanoparticles via structure matching polymerization

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.

Automated summary

This article introduces a new method called the principle of minimization of structure matching polymerization (SMP) for exploring structural and component evolution in nanoscience. The SMP approach allows for rapid exploration of global minimum structures and has important implications for studying the growth mechanisms of nanoparticles. The research also discovered new lowest-energy structures for sulfuric acid-dimethylamine systems, as well as the growth process of boron clusters. The SMP method has significantly improved search efficiency compared to traditional optimization algorithms and can be applied to various problems in material structure design, crystal structure prediction, and drug generation.