ReviewACS polymers Au2022
Data-Driven Methods for Accelerating Polymer Design.
Review in ACS polymers Au, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Modeling Targeted Mechanochemistry in Polymeric Solids.Chemical reviews · 2026Review
- Role of High Fidelity Vs. Low Fidelity Experimental Data in Machine Learning Model Performance for Predicting Polymer Solubility.Macromolecular rapid communications · 2026Article
- Recent Trends in the Development and Clinical Translation of Polymer-based Targeted Therapeutic Nanoparticle.AAPS PharmSciTech · 2026Review
- Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films.Macromolecules · 2026Article
- Architecting Functional Polymers: Advances in Modular Synthesis, Responsive Design, and Multifaceted Applications.Polymers · 2026Review
- Self-Healing Polymer Nanocomposites: Mechanisms, Structure-Property Relationships, and Emerging Applications.Polymers · 2026Review
- Pore Network Percolation in Polymers of Intrinsic Microporosity Examined by Gaseous and Antimatter Probes.Macromolecules · 2025Article
- PMSE Centennial: Celebration of Success and New Frontiers in Polymer Materials Science and Engineering.ACS macro letters · 2025Article
- Review
- Growth vs Diversity: A Time-Evolution Analysis of the Chemical Space.Journal of chemical information and modeling · 2025Article
- Review
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- Design of Recyclable Plastics with Machine Learning and Genetic Algorithm.Journal of chemical information and modeling · 2024Article
- Prediction of Acrylonitrile-Butadiene-Styrene Mechanical Properties through Compressed-Sensing Techniques.Journal of chemical information and modeling · 2024Article
- AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria.Nature communications · 2024Article
- Polymer designChemical science · 2024Article
- Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules.Scientific data · 2024Article
- When Data Are Lacking: Physics-Based Inverse Design of Biopolymers Interacting with Complex, Fluid Phases.Journal of chemical theory and computation · 2024Review
- Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance.ACS polymers Au · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.
Identifiers
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.