ReviewChemical science2020
Can we predict materials that can be synthesised?
Review in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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
18 citing papers in PubMed.
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Exploring Size-Controlled Exciton Evolution Using DNA Libraries.Journal of the American Chemical Society · 2026Article
- Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials.ACS applied materials & interfaces · 2025Article
- Synthesis of Functional Materials Using N-heterocyclic Amines Beyond Melamine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models.Nature communications · 2025Article
- PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals.Scientific data · 2025Article
- Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks.JACS Au · 2025Article
- Crystal structure and characterization of a new one-dimensional copper(II) coordination polymer containing a 4-amino-benzoic acid ligand.Acta crystallographica. Section E, Crystallographic communications · 2024Article
- Confining He Atoms in Diverse Ice-Phases: Examining the Stability of He Hydrate Crystals through DFT Approaches.Molecules (Basel, Switzerland) · 2023Article
- Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials.Chemical science · 2023Review
- Generative Models as an Emerging Paradigm in the Chemical Sciences.Journal of the American Chemical Society · 2023Review
- Computational modeling to assist in the discovery of supramolecular materials.Annals of the New York Academy of Sciences · 2022Review
- Into the Unknown: How Computation Can Help Explore Uncharted Material Space.Journal of the American Chemical Society · 2022Review
- Systematic Comparison of Genetic Algorithm and Basin Hopping Approaches to the Global Optimization of Si(111) Surface Reconstructions.The journal of physical chemistry. A · 2022Article
- High-throughput virtual screening for organic electronics: a comparative study of alternative strategies.Journal of materials chemistry. C · 2021Review
- Materials Precursor Score: Modeling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors.Journal of chemical information and modeling · 2021Article
- On the crystal chemistry of inorganic nitrides: crystal-chemical parameters, bonding behavior, and opportunities in the exploration of their compositional space.Chemical science · 2021Article
- Towards Predictive Synthesis of Inorganic Materials Using Network Science.Frontiers in chemistry · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery of materials is an important element in the development of new technologies and abilities that can help humanity tackle many challenges. Materials discovery is frustratingly slow, with the large time and resource cost often providing only small gains in property performance. Furthermore, researchers are unwilling to take large risks that they will only know the outcome of months or years later. Computation is playing an increasing role in allowing rapid screening of large numbers of materials from vast search space to identify promising candidates for laboratory synthesis and testing. However, there is a problem, in that many materials computationally predicted to have encouraging properties cannot be readily realised in the lab. This minireview looks at how we can tackle the problem of confirming that hypothetical materials are synthetically realisable, through consideration of all the stages of the materials discovery process, from obtaining the components, reacting them to a material in the correct structure, through to processing into a desired form. In an ideal world, a material prediction would come with an associated 'recipe' for the successful laboratory preparation of the material. We discuss the opportunity to thus prevent wasted effort in experimental discovery programmes, including those using automation, to accelerate the discovery of novel materials.
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.