ReviewAdvanced materials (Deerfield Beach, Fla.)2024
Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.
Review in Advanced materials (Deerfield Beach, Fla.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- New Frontiers in AI-Nano Converged Platforms for Intelligent Diagnostics, Therapeutics, and Safety Evaluation.Chem & bio engineering · 2026Review
- A sample model for applying feature selection and machine learning techniques to estimate and manage crayfish populations.Scientific reports · 2026Article
- AI-driven routing and layered architectures for intelligent ICT in nanosensor networked systems.iScience · 2026Review
- Machine Learning-Assisted DNA Origami Shape Sorting Using Fingerprinting Nanosensors and Feature Engineering.Analytical chemistry · 2026Article
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- The role of the tumor microenvironment in mediating radiopharmaceutical therapy: bridging nuclear medicine and cancer immunotherapy.Military Medical Research · 2026Review
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- Multi-Dimensional Synergistic Engineering for Boosting Nanozyme Catalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- 33 Unresolved Questions in Nanoscience and Nanotechnology.ACS nano · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- A Review on Biomedical Applications of Plant Extract-Mediated Metallic Ag, Au, and ZnO Nanoparticles and Future Prospects for Their Combination with Graphitic Carbon Nitride.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Recent Advances in Chiral Gold Nanomaterials: From Synthesis to Applications.Molecules (Basel, Switzerland) · 2025Review
- Bio-Informed Porous Mineral-Based Composites.Small (Weinheim an der Bergstrasse, Germany) · 2025Review
- Green carbon dots in the era of AI: sustainable synthesis, intelligent drug delivery, advanced diagnostics, and bioimaging.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case.Angewandte Chemie (International ed. in English) · 2024Article
- Toward Machine-Learning-Accelerated Design of All-Dielectric Magnetophotonic Nanostructures.ACS applied materials & interfaces · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.
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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.