Elucidating dominant pathways of the nano-particle self-assembly process

Zeng, Xiangze; Li, Bin; Qiao, Qin; Zhu, Lizhe; Lu, Zhong‐Yuan; Huang, Xuhui

doi:10.1039/c6cp01808d

Cited by 23 publications

(28 citation statements)

References 46 publications

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“…As listed in Supplementary Table 1 , the initial candidate CVs describe various structural and geometric features of the ice nucleus. The structural features (including the molecular numbers of hexagonal and rhombic ice) were obtained by using average bond order parameters 85 to distinguish the local structures of each water molecule (see Methods and Supplementary Methods 2 for more detail), and the spherical parameter 77 was used to monitor the geometric evolution of the ice nucleus. As illustrated in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, MSMs have become a popular and powerful tool to simulate biomolecular dynamics, such as RNA 63 and protein [67][68][69][70][71][72] folding, ligand-receptor binding [73][74][75] , and protein allostery 76 . In addition, researchers, including ourselves, have previously applied MSMs to clarify the dominant self-assembly pathways of nanoparticles 77,78 and lipids 79 , where MSMs can automatically identify intermediate states and calculate their respective thermodynamic and kinetic properties at equilibrium. To our knowledge, MSMs have not been previously used to elucidate the kinetics of ice nucleation.…”

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confidence: 99%

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Temperature-dependent kinetic pathways of heterogeneous ice nucleation competing between classical and non-classical nucleation

et al. 2021

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Ice nucleation on the surface plays a vital role in diverse areas, ranging from physics and cryobiology to atmospheric science. Compared to ice nucleation in the bulk, the water-surface interactions present in heterogeneous ice nucleation complicate the nucleation process, making heterogeneous ice nucleation less comprehended, especially the relationship between the kinetics and the structures of the critical ice nucleus. Here we combine Markov State Models and transition path theory to elucidate the ensemble pathways of heterogeneous ice nucleation. Our Markov State Models reveal that the classical one-step and non-classical two-step nucleation pathways can surprisingly co-exist with comparable fluxes at T = 230 K. Interestingly, we find that the disordered mixing of rhombic and hexagonal ice leads to a favorable configurational entropy that stabilizes the critical nucleus, facilitating the non-classical pathway. In contrast, the favorable energetics promotes the formation of hexagonal ice, resulting in the classical pathway. Furthermore, we discover that, at elevated temperatures, the nucleation process prefers to proceed via the classical pathway, as opposed to the non-classical pathway, since the potential energy contributions override the configurational entropy compensation. This study provides insights into the mechanisms of heterogeneous ice nucleation and sheds light on the rational designs to control crystallization processes.

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Section: Resultsmentioning

confidence: 99%

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Temperature-dependent kinetic pathways of heterogeneous ice nucleation competing between classical and non-classical nucleation

et al. 2021

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“…吕中元教授等 [62] )随时间的变化 [62] (网络版彩图) [74,75] . 为了实现在更大时间和空间尺度上有效模拟软补丁粒子的聚集行为, 李等 [73,76] 开发了软补丁粒子模型(soft patchy particle models, SSPMs [44] (网络版彩图) [78] (网络版彩图) [15,47] 用Wang-Landau方法 [79,80] 获取了…”

Section: 布朗动力学模拟两亲性嵌段共聚物自组装unclassified

“…Hagan等 [46] 和Frenkel等 [47] 提出, 用无向图来表示聚集体, 根据图的拓扑结构来划分构象空间. 为了找出自组装体系中态与态之间的转移路径, 我们提出了"质量流(mass flow)"的概念, 用来表示聚集体在不同小态间的转移质量 [44] . 基于"质量流"矩阵, 我们可以用修…”

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Multiscale simulation and kinetic network model analysis of the self-assembly of amphiphilic systems

Zheng

Zeng

2020

Sci. Sin.-Chim

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“…The APM algorithm has been widely applied to investigate multi-body processes with heterogeneous timescales [86,93], such as the loading of miRNA to the human Argonaute protein [94,95] and the self-assembly of the star-like block copolymers nano-particle [96] and the β-sheet-rich amyloid-β dimers [97]. For example, in the work by Jiang et al [94], the authors applied the APM algorithm to analyze the molecular mechanisms of how human Agonaute protein recognizes and loads miRNA.…”

Section: The K-centers Algorithmmentioning

confidence: 99%

Clustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems

Peng

Wang

et al. 2018

Chinese Journal of Chemical Physics

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Molecular dynamics (MD) simulation has become a powerful tool to investigate the structurefunction relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets containing millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape (geometric clustering) and kinetics (kinetic clustering). In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, agglomerative algorithms (single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage), center-based algorithms (K-Means, K-Medoids, K-Centers, and APM), density-based algorithms (neighbor-based, DBSCAN, density-peaks, and Robust-DB), and spectral-based algorithms (PCCA and PCCA+). In particular, differences between geometric and kinetic clustering metrics will be discussed along with the performances of different clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algorithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.

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Elucidating dominant pathways of the nano-particle self-assembly process

Cited by 23 publications

References 46 publications

Temperature-dependent kinetic pathways of heterogeneous ice nucleation competing between classical and non-classical nucleation

Temperature-dependent kinetic pathways of heterogeneous ice nucleation competing between classical and non-classical nucleation

Multiscale simulation and kinetic network model analysis of the self-assembly of amphiphilic systems

Clustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems

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