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Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Published on: February 6, 2020

Nonlinear machine learning of patchy colloid self-assembly pathways and mechanisms.

Andrew W Long¹, Andrew L Ferguson

¹Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States.

The Journal of Physical Chemistry. B

|March 26, 2014

Summary

This study introduces a novel data mining approach using diffusion maps to systematically uncover self-assembly pathways. The method successfully identifies distinct routes for forming tetrahedral and icosahedral structures from patchy colloids.

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Area of Science:

Materials Science
Chemical Physics
Computational Chemistry

Background:

Bottom-up self-assembly is crucial for synthesizing complex materials.
Understanding self-assembly pathways and mechanisms is key for designing new materials and understanding fundamental principles.
Current methods for analyzing assembly pathways can be limited and require manual inspection.

Purpose of the Study:

To develop and validate a systematic approach for inferring self-assembly pathways and mechanisms.
To apply nonlinear data mining of molecular simulation trajectories to understand material self-assembly.
To guide the inverse design of building blocks for self-assembling materials.

Main Methods:

Utilized nonlinear data mining techniques, specifically diffusion maps, on molecular simulation trajectories.
Applied the methodology to Brownian dynamics simulations of anisotropic 'patchy colloids'.
Analyzed the assembly of particles into polyhedral aggregates, including tetrahedral and icosahedral structures.

Main Results:

Identified two distinct assembly pathways for tetrahedral aggregates: chains of dimers/tetramers and chains of trimers.
Recovered two assembly pathways for icosahedral aggregates: monomeric addition and budding from a liquid phase.
Validated the approach by successfully reproducing previously identified assembly routes.

Conclusions:

The diffusion map approach systematically and reliably infers self-assembly mechanisms.
This computational method enhances the understanding of self-assembly processes.
The validated methodology provides a powerful tool for designing self-assembling materials.