ReviewAdvanced materials (Deerfield Beach, Fla.)2021
Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery.
Review in Advanced materials (Deerfield Beach, Fla.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 74 citations in OpenAlex.
- Learning ordinality-aware multimodal representations for composite materials design.Nature communications · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- A unified mechanistic perspective on protein-polysaccharide interactions underlying hybrid gel formation: A mini review.Current research in food science · 2026Review
- Accurate and rapid measurement of fluid thermal conductivity.Nature communications · 2025Article
- Enabling technology for accelerated discovery of supramolecular materials.Chemical science · 2025Review
- Synthesis of Functional Materials Using N-heterocyclic Amines Beyond Melamine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Design and Synthesis of Red-Absorbing Fluoran Leuco Dyes Supported by Computational Screening.ACS omega · 2024Article
- Mesophase Behavior of Molecules Containing Three Benzene Rings Connected via Imines and Ester Linkages.ACS omega · 2024Article
- Materials discovery with extreme propertiesChemical science · 2024Article
- Argentophilic Interactions, Flexibility, and Dynamics of Pyrrole Cages Encapsulating Silver(I) Clusters.The journal of physical chemistry. A · 2024Article
- Systematic exploration of accessible topologies of cage moleculesChemical science · 2023Article
- Computational modeling to assist in the discovery of supramolecular materials.Annals of the New York Academy of Sciences · 2022Review
- Coarse-grained modelling to predict the packing of porous organic cages.Chemical science · 2022Article
- Into the Unknown: How Computation Can Help Explore Uncharted Material Space.Journal of the American Chemical Society · 2022Review
- Purely Covalent Molecular Cages and Containers for Guest Encapsulation.Chemical reviews · 2022Review
- Unlocking the computational design of metal-organic cages.Chemical communications (Cambridge, England) · 2022Review
- Artificial intelligence: A powerful paradigm for scientific research.Innovation (Cambridge (Mass.)) · 2021Review
- Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning.Chemical science · 2021Review
- Materials Precursor Score: Modeling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors.Journal of chemical information and modeling · 2021Article
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 at 1 institution in 1 country.
Funding
Abstract
Organic materials find application in a range of areas, including optoelectronics, sensing, encapsulation, molecular separations, and photocatalysis. The discovery of materials is frustratingly slow however, particularly when contrasted to the vast chemical space of possibilities based on the near limitless options for organic molecular precursors. The difficulty in predicting the material assembly, and consequent properties, of any molecule is another significant roadblock to targeted materials design. There has been significant progress in the development of computational approaches to screen large numbers of materials, for both their structure and properties, helping guide synthetic researchers toward promising materials. In particular, artificial intelligence techniques have the potential to make significant impact in many elements of the discovery process. Alongside this, automation and robotics are increasing the scale and speed with which materials synthesis can be realized. Herein, the focus is on demonstrating the power of integrating computational and experimental materials discovery programmes, including both a summary of key situations where approaches can be combined and a series of case studies that demonstrate recent successes.
Indexed as
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.