Coarse-grained modeling of nanocube self-assembly system and transition network analyses

Imamura, Kosuke; Yamamoto, Takeshi; Sato, Hirofumi

doi:10.1016/j.cplett.2020.137135

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…A subsequent study with the newly developed implicit solvent model was performed by Imamura et al for capturing the nanocube self-assembly process more comprehensively. 67 While it was found that the CG model, which is more advantageous than all-atom model in the calculation efficiency, reasonably explains the solvent effect, the standard CG-MD simulation failed the reproduction of the nanocube structure. On the other hand, replica exchange CG-MD simulation successfully reproduced a highly ordered nanocube structure.…”

Section: Molecular Dynamicsmentioning

confidence: 99%

Theoretical and computational methodologies for understanding coordination self-assembly complexes

Takahashi

Iuchi

Hiraoka

et al. 2023

Phys. Chem. Chem. Phys.

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Section: Molecular Dynamicsmentioning

confidence: 99%

Theoretical and computational methodologies for understanding coordination self-assembly complexes

Takahashi

Iuchi

Hiraoka

et al. 2023

Phys. Chem. Chem. Phys.

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“…And mean first passage time (MFPT), defined as the average time for the first transition between metastable states, can also be computed along these kinetic pathways. In recent years, KNM has become increasingly popular in material science and been adopted to study the dynamics of self-assembly, such as ice nucleation (47), colloidal particles (48)(49)(50), and amphiphiles assembly (51)(52)(53)(54). However, there still remain two major challenges for its applications in self-assembly (16).…”

Section: Introductionmentioning

confidence: 99%

GraphVAMPnets for Uncovering Slow Collective Variables of Self-Assembly Dynamics

Liu

Xue

Qiu

et al. 2023

Preprint

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Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack the high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenge. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, like tICA, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets which combines graph neural networks with variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in a high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory that studies Markov processes without forcing detailed balance constraint, which addresses the out-of-equilibrium challenge in self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: aggregation of two hydrophobic molecules and self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely to applied to molecular self-assembly.

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Coarse Grained MD Simulations of Soft Matter

Shi,

Qian,

2024

Comprehensive Computational Chemistry

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Coarse-grained modeling of nanocube self-assembly system and transition network analyses

Cited by 6 publications

References 34 publications

Theoretical and computational methodologies for understanding coordination self-assembly complexes

Theoretical and computational methodologies for understanding coordination self-assembly complexes

GraphVAMPnets for Uncovering Slow Collective Variables of Self-Assembly Dynamics

Coarse Grained MD Simulations of Soft Matter

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