ReviewNature materials2021
From predictive modelling to machine learning and reverse engineering of colloidal self-assembly.
Review in Nature materials, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Supramolecular strategy in traditional Chinese medicine processing: Mechanistic insights mediated by self-assembly.Acta pharmaceutica Sinica. B · 2026Review
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Interfacial depletant partitioning for lattice-tunable colloidal assembly.Nature communications · 2026Article
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Fractional crystallization of colloids by sequential tuning of depletion forces.Nature communications · 2026Article
- A DNA-encoded recipe to direct multistage colloidal assembly.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Understanding Coupling in Hierarchically Doped Plasmonic Nanocrystal Metamaterials.ACS materials Au · 2026Article
- Liquid crystal-driven interfacial ordering of colloidal microplastics: Advancing microplastic characterization below the macroscale.Science advances · 2025Article
- Machine-Assisted Inverse Design of Patchy Particles for Self-Assembly of Archimedean Tilings.ACS nano · 2025Article
- Inverse Thermodynamics: Designing Interactions for Targeted Phase Behavior.The journal of physical chemistry. B · 2025Article
- Encoding hierarchical 3D architecture through inverse design of programmable bonds.Nature materials · 2025Article
- Confusion-Driven Machine Learning of Structural Phases of a Flexible, Magnetic Stockmayer Polymer.Journal of chemical theory and computation · 2025Article
- Pushing the Frontiers: Artificial Intelligence (AI)-Guided Programmable Concepts in Binary Self-Assembly of Colloidal Nanoparticles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Perspective: Thermophoresis and Its Promise for Optical Patterning.Langmuir : the ACS journal of surfaces and colloids · 2025Review
- Distribution of Single-Particle Resonances Determines the Plasmonic Response of Disordered Nanoparticle Ensembles.ACS nano · 2024Article
- Self-Assembly in an Experimentally Realistic Model of Lobed Patchy Colloids.ACS applied bio materials · 2024Review
- Article
- Neural functional theory for inhomogeneous fluids: Fundamentals and applications.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Simultaneous and independent topological control of identical microparticles in non-periodic energy landscapes.Nature communications · 2023Article
- Condensed-Phase Molecular Representation to Link Structure and Thermodynamics in Molecular Dynamics.Journal of chemical theory and computation · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An overwhelming diversity of colloidal building blocks with distinct sizes, materials and tunable interaction potentials are now available for colloidal self-assembly. The application space for materials composed of these building blocks is vast. To make progress in the rational design of new self-assembled materials, it is desirable to guide the experimental synthesis efforts by computational modelling. Here, we discuss computer simulation methods and strategies used for the design of soft materials created through bottom-up self-assembly of colloids and nanoparticles. We describe simulation techniques for investigating the self-assembly behaviour of colloidal suspensions, including crystal structure prediction methods, phase diagram calculations and enhanced sampling techniques, as well as their limitations. We also discuss the recent surge of interest in machine learning and reverse-engineering methods. Although their implementation in the colloidal realm is still in its infancy, we anticipate that these data-science tools offer new paradigms in understanding, predicting and (inverse) design of novel colloidal materials.
Identifiers
34045705What OpenQuestion holds
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.