ArticleCommunications chemistry2022
Classifying soft self-assembled materials via unsupervised machine learning of defects.
Article in Communications chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in Dynamic/Adaptive Supramolecular Self-Assembly: From Molecular Design to Stimuli-Responsive Control.Macromolecular rapid communications · 2026Pooled it
- A Deep Neural Network Potential Study on the Mechanical and Thermophysical Properties of β‑HMX Molecular Crystal.ACS omega · 2026Article
- Monomer exchange dynamics in ureido-pyrimidinone supramolecular polymersJournal of materials chemistry. B · 2025Article
- Relevant, Hidden, and Frustrated Information in High-Dimensional Analyses of Complex Dynamical Systems with Internal Noise.Journal of chemical theory and computation · 2025Article
- New Coarse-Grained Models to Describe the Self-Assembly of Aqueous Aerosol-OT.The journal of physical chemistry. B · 2025Article
- Classification and spatiotemporal correlation of dominant fluctuations in complex dynamical systems.PNAS nexus · 2025Article
- Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning.Biophysical journal · 2024Article
- Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Sampling Real-Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles.Communications chemistry · 2023Article
- Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning.ACS polymers Au · 2023Article
- Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles.The journal of physical chemistry. B · 2023Article
- Supramolecular Semiconductivity through Emerging Ionic Gates in Ion-Nanoparticle Superlattices.ACS nano · 2023Article
- Computational modeling to assist in the discovery of supramolecular materials.Annals of the New York Academy of Sciences · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Unlike molecular crystals, soft self-assembled fibers, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them, e.g., capable to communicate with each other. However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven "defectometer" to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.