ReviewACS nano2022
Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis.
Review in ACS nano, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Nanostructured Ion Conductor Design for Highly Efficient Ion Transport.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Measurement-Aware Computational Modeling for Optical Spectrometry in Scattering-Dominated Systems.ACS omega · 2026Article
- Multifunctional nanotherapeutics for tumor microenvironment modulation in solid tumor therapy.Discover nano · 2026Review
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Engineered MgO Nanoparticles with Tunable Electronic Signatures for Energy Applications.ACS omega · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Self-assembling nanocomposites for smart drug delivery: towards personalized and stimuli-responsive therapeutics.RSC advances · 2026Review
- Atom-level descriptors and explainable prediction of iron carbide nanoparticles' cytotoxicityNanoscale advances · 2026Article
- The Evolution of Nanoparticle Regulation: A Meta-Analysis of Research Trends and Historical Parallels (2015-2025).Nanomaterials (Basel, Switzerland) · 2026Article
- Safe and sustainable by design with ML/AI: A transformative approach to advancing nanotechnology.Beilstein journal of nanotechnology · 2026Article
- Accelerated data-driven materials science with the Materials Project.Nature materials · 2025Review
- Intelligent Systems for Inorganic Nanomaterial Synthesis.Nanomaterials (Basel, Switzerland) · 2025Review
- Chemoresistive Gas Sensors Based on Noble-Metal-Decorated Metal Oxide Semiconductors for HMaterials (Basel, Switzerland) · 2025Review
- Biogenic nanoparticles: pioneering a new era in breast cancer therapeutics-a comprehensive review.Discover nano · 2024Review
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Intelligent nanomaterials for cancer therapy: recent progresses and future possibilities.Medical review (2021) · 2023Review
- Synergistic impact of nanomaterials and plant probiotics in agriculture: A tale of two-way strategy for long-term sustainability.Frontiers in microbiology · 2023Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.
Indexed as
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What 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.