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Review
. 2025 Jul;12(28):e2501000.
doi: 10.1002/advs.202501000. Epub 2025 Apr 26.

Pushing the Frontiers: Artificial Intelligence (AI)-Guided Programmable Concepts in Binary Self-Assembly of Colloidal Nanoparticles

Cancan Li  1 , Lindong Ma  1 , Zhenjie Xue  1 , Xiao Li  1 , Shan Zhu  1 , Tie Wang  1
Affiliations
Review

Pushing the Frontiers: Artificial Intelligence (AI)-Guided Programmable Concepts in Binary Self-Assembly of Colloidal Nanoparticles

Cancan Li et al. Adv Sci (Weinh). 2025 Jul.

Abstract

Colloidal nanoparticle self-assembly is a key area in nanomaterials science, renowned for its ability to design metamaterials with tailored functionalities through a bottom-up approach. Over the past three decades, advancements in nanoparticle synthesis and assembly control methods have propelled the transition from single-component to binary assemblies. While binary assembly has been recognized as a significant concept in materials design, its potential for intelligent and customized assembly has often been overlooked. It is argued that the future trend in the assembly of binary nanocrystalline superlattices (BNLSs) can be analogous to the '0s' and '1s' in computer programming, and customizing their assembly through precise control of these basic units could significantly expand their application scope. This review briefly recaps the developmental trajectory of nanoparticle assembly, tracing its evolution from simple single-component assemblies to complex binary co-assemblies and the unique property changes they induce. Of particular significance, this review explores the future prospects of binary co-assembly, viewed through the lens of 'AI-guided programmable assembly'. Such an approach has the potential to shift the paradigm from passive assembly to active, intelligent design, leading to the creation of new materials with disruptive properties and functionalities and driving profound changes across multiple high-tech fields.

Keywords: artificial intelligence; binary co‐assembly; colloidal nanoparticles; intelligent novel materials; self‐assembly; superstructural materials.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
AI‐guided programmable idea self‐assembly of multiple colloidal nanocrystalline materials.
Figure 2
Figure 2
a) Schematic illustration of the self‐assembly mechanism at the gas‐liquid interface. b) Graphic representation of the microemulsion formation and assembly procedure. c) Visual depiction of the assembly process driven by solvent evaporation. d) Illustrative diagram of the induced self‐assembly mechanism.
Figure 3
Figure 3
a) Schematic illustration of the method for fabricating SP superlattices using the template‐induced self‐assembly and the schematic illustration of the growth of gas bubbles on a flat film electrode, which caused a large number of inactive sites to form. Reproduced with permission.[ 43 ] Copyright 2019, ACS. b) Fabrication of patterned AuNP superlattice film by template‐induced self‐assembly. Reproduced with permission.[ 44 ] Copyright 2024, ACS.
Figure 4
Figure 4
a) Scheme of needle‐like superparticle synthesis. b–c) Scanning electron microscope (SEM) image and TEM image of needle‐like superparticle. d) PL intensity versus polarization angle as the polarization was manually rotated while measuring a typical superparticle‐embedded PDMS thin film under the excitation wavelength of 380 nm. Reproduced with permission.[ 1c ] Copyright 2012, AAAS. e) Schematic of the build‐up process of superfluorescence. Reproduced with permission.[ 7b ] Copyright 2018, Springer Nature.
Figure 5
Figure 5
a) Schematic diagram of a single superlattice projected along the z direction and high‐angle toroidal dark‐field scanning transmission electron microscopy image. Scale, 200nm. b) Preparation process of superlattice array. Reproduced with permission.[ 20 ] Copyright 2022, AAAS. c) Schematic of the preparation of the CsPbBr3 single‐crystal microstructure array via NSALS. d) Detailed characterization of the CsPbBr3 single‐crystal microstructure array. e) Schematics of the CsPbBr3 single‐crystal microstructure array photodetector and the optical microscopy image of the photodetector prepared using a gold patch electrode; scale bar: 5 µm. Reproduced with permission.[ 64 ] Copyright 2024, ACS.
Figure 6
Figure 6
a) Schematic diagram of the preparation of GSPs@ZIF‐8 and schematic diagram of the detection of volatile organic compound (VOC) via surface‐enhanced Raman scattering (SERS) spectroscopy. b) Schematic illustration of GSPs and GSPs@ZIF‐8 with gas collisions. Reproduced with permission.[ 1 ] Copyright 2018, Wiley‐VCH. c) Schematic diagram of the synthetic route of GSPs@H‐ZIF‐8. d) Diagram of a SERS sensor for VOC detection and digital photos of paper‐based SERS substrate and mask sensing device for the breath test. Reproduced with permission.[ 66 ] Copyright 2022, Wiley‐VCH.
Figure 7
Figure 7
a) TEM images of (001) projections of BNSLs self‐assembled from 8.7 nm CdSe and 5.5 nm Au nanoparticles and fluorescence spectroscopy. Reproduced with permission.[ 31 ] Copyright 2008, ACS. b) Emission spectrum of ReO3‐type SLs and lamellar SLs employing 8.6 nm CsPbBr3 nanoparticles and 9 nm LaF3 disks. Reproduced with permission.[ 67 ] Copyright 2021, ACS. c) Schematic illustration showing the formation of Janus PCSs from an aqueous droplet containing nonmagnetic spheres and magnetic ellipsoids. Reproduced with permission.[ 71 ] Copyright 2021, RSC.
Figure 8
Figure 8
a) Schematic illustration of the self‐assembly process used to synthesize the Au/CdSe NCs. Reproduced with permission.[ 72 ] Copyright 2017, Wiley‐VCH. b) Schematic of hybrid aggregates emphasizing the role of their compartmentalization and sorption capabilities. Reproduced with permission.[ 26 ] Copyright 2021, Springer Nature. c) HAADF‐STEM image and the corresponding elemental mapping of AB13‐type CoFe2O4−Fe3O4 binary superparticles. Reproduced with permission.[ 13 ] Copyright 2018, ACS.
Figure 9
Figure 9
a) A multifunctional polymer nanomedicine platform for both MRI and optical imaging of cancer and effective in vitro drug delivery. Reproduced with permission.[ 73 ] Copyright 2008, Wiley‐VCH. b) Synthesis, characterizations, and biological applications of core‐shell‐structured SPs. Reproduced with permission.[ 1 ] Copyright 2014, Springer Nature. c) Superparamagnetic and Bioactive Multicore–Shell Nanoparticles (γ‐Fe2O3@SiO2‐CaO) can repair bone tissue at the same time as being used as a cancer treatment. Reproduced with permission.[ 74 ] Copyright 2020, ACS.
Figure 10
Figure 10
a) Workflow of ML‐guided synthesis of carbon quantum dots (CQDs) with superior optical properties. Reproduced with permission.[ 78 ] Copyright 2024, Springer Nature. b) Closed‐loop optimization for the discovery of quaternary metallic SINPs. Reproduced with permission.[ 79 ] Copyright 2021, AAAS. c) Schematic diagram of the artificial intelligence‐driven Carbon Copilot (CARCO) platform. Reproduced with permission.[ 80 ] Copyright 2025, Cell.
Figure 11
Figure 11
a) Schematic diagram of binary assembly and TEM image of binary self‐assembly formed by the interaction of tripodal nanoplates and rhombic nanoplates through complementary shapes. Reproduced with permission.[ 11 ] Copyright 2013, ACS. b) Schematic and in situ TEM of depletion‐induced tunable assembly of complementary platonic solids. Reproduced with permission.[ 82 ] Copyright 2024, ACS. c) 3D NaCl‐type binary porous superstructures formed by the co‐assembly of two colloidal polyhedral metal‐organic framework (MOF) particles with complementary sizes, shapes, and charges. Reproduced with permission.[ 69 ] Copyright 2024, ACS.
Figure 12
Figure 12
a) ESP model validation enzyme‐substrate relationship diagram. Reproduced with permission.[ 87 ] Copyright 2023, Springer Nature. b) Schematic diagram of the GraphBNC method combining graph theory and neural networks to predict atomic‐level interactions between metal nanoclusters and proteins. Reproduced with permission.[ 87 ] Copyright 2024, Wiley‐VCH. c) Schematic representation of RoseTTAFold All‐Atom predictions of biomolecular assemblies, including proteins, nucleic acids, metals, small molecules, and covalent modifications. Reproduced with permission.[ 88 ] Copyright 2024, AAAS. d) Schematic representation of the feedback mechanism in the dark reactions project and the graphical representation of the three hypotheses generated from the model, and representative structures for each hypothesis. Reproduced with permission.[ 89 ] Copyright 2016, Springer Nature.
Figure 13
Figure 13
a) TEM characterisation of the assemblies of sharp nanocubes and round nanocubes, with computer simulations. Reproduced with permission.[ 102 ] Copyright 2018, Springer Nature. b) Nucleation and growth of the binary icosahedral cluster in simulations. Reproduced with permission.[ 35 ] Copyright 2021, Springer Nature. c) Schematic of an AFM tip scanning a high topography with high‐resolution features. Reproduced with permission.[ 107 ] Copyright 2021, Springer Nature. d) Evo foundation model schematic and Pretraining a genomic foundation model across prokaryotic life. Reproduced with permission.[ 108 ] Copyright 2024, AAAS.
Figure 14
Figure 14
Summary diagram of the preamble technology for AI‐guided assemblies.
Figure 15
Figure 15
a) External electric control of the robotic arm, two pointer extension designs for the robot arm, and corresponding TEM images. Reproduced with permission.[ 111 ] Copyright 2018, AAAS. b) The schematic diagram and demonstration of HGNPCs as potential external wearable assistive applications. Reproduced with permission.[ 114 ] Copyright 2024, Wiley‐VCH. c) Instrument schematic of the custom‐built optical tweezer and DF microscope. Reproduced with permission.[ 117 ] Copyright 2023, ACS. d) Schematic showing the main elements of the experimental apparatus and the illustration of the nanodiamond self‐assembly process, where the particles assemble at the focus of infrared optical tweezers. Reproduced with permission.[ 116 ] Copyright 2024, ACS.
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